Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

Abstract The objective of this work is to improve the quality of the information that belongs to the database CubaCiencia, of the Institute of Scientific and Technological Information. This database has bibliographic information referring to four segments of science and is the main database of the Library Management System. The applied methodology was based on the Decision Trees, the Correlation Matrix, the 3D Scatter Plot, etc., which are techniques used by data mining, for the study of large volumes of information. The results achieved not only made it possible to improve the information in the database, but also provided truly useful patterns in the solution of the proposed objectives.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

Nearly two thirds of surveyed top managers of large companies operating in Russia viewed process mining as useful for purchasing, in 2021. Furthermore, over ** percent of respondents saw the technology's potential in improving the customer journey map and IT processes.

The LSC (Leicester Scientific Corpus)

April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data are extracted from the Web of Science [1]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.[Version 2] A further cleaning is applied in Data Processing for LSC Abstracts in Version 1*. Details of cleaning procedure are explained in Step 6.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v1.Getting StartedThis text provides the information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the meaning of research texts and make it available for use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. The corpus contains only documents in English. Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper 3. Abstract: The abstract of the paper 4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’. 5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’. 6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4] 7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018. We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,350.Data ProcessingStep 1: Downloading of the Data Online

The dataset is collected manually by exporting documents as Tab-delimitated files online. All documents are available online.Step 2: Importing the Dataset to R

The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryAs our research is based on the analysis of abstracts and categories, all documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsEspecially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc. Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. The detection and identification of such words is done by sampling of medicine-related publications with human intervention. Detected concatenate words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.The section headings in such abstracts are listed below:

Background Method(s) Design Theoretical Measurement(s) Location Aim(s) Methodology Process Abstract Population Approach Objective(s) Purpose(s) Subject(s) Introduction Implication(s) Patient(s) Procedure(s) Hypothesis Measure(s) Setting(s) Limitation(s) Discussion Conclusion(s) Result(s) Finding(s) Material (s) Rationale(s) Implications for health and nursing policyStep 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction, the lengths of abstracts are calculated. ‘Length’ indicates the total number of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. In LSC, we decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis.

Step 6: [Version 2] Cleaning Copyright Notices, Permission polices, Journal Names and Conference Names from LSC Abstracts in Version 1Publications can include a footer of copyright notice, permission policy, journal name, licence, author’s right or conference name below the text of abstract by conferences and journals. Used tool for extracting and processing abstracts in WoS database leads to attached such footers to the text. For example, our casual observation yields that copyright notices such as ‘Published by Elsevier ltd.’ is placed in many texts. To avoid abnormal appearances of words in further analysis of words such as bias in frequency calculation, we performed a cleaning procedure on such sentences and phrases in abstracts of LSC version 1. We removed copyright notices, names of conferences, names of journals, authors’ rights, licenses and permission policies identified by sampling of abstracts.Step 7: [Version 2] Re-extracting (Sub-setting) the Data Based on Lengths of AbstractsThe cleaning procedure described in previous step leaded to some abstracts having less than our minimum length criteria (30 words). 474 texts were removed.Step 8: Saving the Dataset into CSV FormatDocuments are saved into 34 CSV files. In CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/ [2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html [4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US [5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3 [6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.

The Supplementary Material of the paper "Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining" includes: 1) APPENDIX 1: SQL Statements for data extraction. Appendix 2: Interview for operating Staff. 2) The DataSet of the normalized data to define the predictive model.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

The number of publications of ^ per year. The percentile is given for the sake of comparison with the literature.

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Companies in this industry develop software for data mining. Data mining is the process of extracting patterns from large data sets.

Background

Any set of related activities that are executed in a repeatable manner and with a defined goal can be seen as process.

Process analytic approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support managerial-decision making across the organization.

For organisations interested in continuous improvement, such datasets allow data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of ‘current deployed process’ (whose execution trace is available), is critical to knowing whether it is worth investing in improvements, where performance problems exist, and how much variation there is in the process across the instances and what are the root-causes.

What is Process Mining (PM) ?

→ process of extracting valuable information from event logs/databases that are generated by processes.

Two topics are important i) process discovery where a process model describing the control flow is inferred from the data and ii) of conformance checking which deals with verifying that the behavior in the event log adheres to a set of business rules, e.g., defined as a process model. Rhese two use cases focus on the control-flow perspective,

Why Process Mining ?

→ identifying hidden nodes and bottlenecks in business processes.

About the Dataset

A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes.

Noise is added by randomly adding one additional event to an increasing number of traces. CPN Tools (http://cpntools.org) was used to generate the event log and inject the noise. The amount of noise can be controlled with the constant 'noise'.

Smaller dataset:

The files test0 to test5 represent process traces and maybe used for debugging and sanity check purposes

LScD (Leicester Scientific Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScD (Leicester Scientific Dictionary) is created from the updated LSC (Leicester Scientific Corpus) - Version 2*. All pre-processing steps applied to build the new version of the dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. After pre-processing steps, the total number of unique words in the new version of the dictionary is 972,060. The files provided with this description are also same as described as for LScD Version 2 below.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2** Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v2[Version 2] Getting StartedThis document provides the pre-processing steps for creating an ordered list of words from the LSC (Leicester Scientific Corpus) [1] and the description of LScD (Leicester Scientific Dictionary). This dictionary is created to be used in future work on the quantification of the meaning of research texts. R code for producing the dictionary from LSC and instructions for usage of the code are available in [2]. The code can be also used for list of texts from other sources, amendments to the code may be required.LSC is a collection of abstracts of articles and proceeding papers published in 2014 and indexed by the Web of Science (WoS) database [3]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English. The corpus was collected in July 2018 and contains the number of citations from publication date to July 2018. The total number of documents in LSC is 1,673,824.LScD is an ordered list of words from texts of abstracts in LSC.The dictionary stores 974,238 unique words, is sorted by the number of documents containing the word in descending order. All words in the LScD are in stemmed form of words. The LScD contains the following information:1.Unique words in abstracts2.Number of documents containing each word3.Number of appearance of a word in the entire corpusProcessing the LSCStep 1.Downloading the LSC Online: Use of the LSC is subject to acceptance of request of the link by email. To access the LSC for research purposes, please email to ns433@le.ac.uk. The data are extracted from Web of Science [3]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.Step 2.Importing the Corpus to R: The full R code for processing the corpus can be found in the GitHub [2].All following steps can be applied for arbitrary list of texts from any source with changes of parameter. The structure of the corpus such as file format and names (also the position) of fields should be taken into account to apply our code. The organisation of CSV files of LSC is described in README file for LSC [1].Step 3.Extracting Abstracts and Saving Metadata: Metadata that include all fields in a document excluding abstracts and the field of abstracts are separated. Metadata are then saved as MetaData.R. Fields of metadata are: List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.Step 4.Text Pre-processing Steps on the Collection of Abstracts: In this section, we presented our approaches to pre-process abstracts of the LSC.1.Removing punctuations and special characters: This is the process of substitution of all non-alphanumeric characters by space. We did not substitute the character “-” in this step, because we need to keep words like “z-score”, “non-payment” and “pre-processing” in order not to lose the actual meaning of such words. A processing of uniting prefixes with words are performed in later steps of pre-processing.2.Lowercasing the text data: Lowercasing is performed to avoid considering same words like “Corpus”, “corpus” and “CORPUS” differently. Entire collection of texts are converted to lowercase.3.Uniting prefixes of words: Words containing prefixes joined with character “-” are united as a word. The list of prefixes united for this research are listed in the file “list_of_prefixes.csv”. The most of prefixes are extracted from [4]. We also added commonly used prefixes: ‘e’, ‘extra’, ‘per’, ‘self’ and ‘ultra’.4.Substitution of words: Some of words joined with “-” in the abstracts of the LSC require an additional process of substitution to avoid losing the meaning of the word before removing the character “-”. Some examples of such words are “z-test”, “well-known” and “chi-square”. These words have been substituted to “ztest”, “wellknown” and “chisquare”. Identification of such words is done by sampling of abstracts form LSC. The full list of such words and decision taken for substitution are presented in the file “list_of_substitution.csv”.5.Removing the character “-”: All remaining character “-” are replaced by space.6.Removing numbers: All digits which are not included in a word are replaced by space. All words that contain digits and letters are kept because alphanumeric characters such as chemical formula might be important for our analysis. Some examples are “co2”, “h2o” and “21st”.7.Stemming: Stemming is the process of converting inflected words into their word stem. This step results in uniting several forms of words with similar meaning into one form and also saving memory space and time [5]. All words in the LScD are stemmed to their word stem.8.Stop words removal: Stop words are words that are extreme common but provide little value in a language. Some common stop words in English are ‘I’, ‘the’, ‘a’ etc. We used ‘tm’ package in R to remove stop words [6]. There are 174 English stop words listed in the package.Step 5.Writing the LScD into CSV Format: There are 1,673,824 plain processed texts for further analysis. All unique words in the corpus are extracted and written in the file “LScD.csv”.The Organisation of the LScDThe total number of words in the file “LScD.csv” is 974,238. Each field is described below:Word: It contains unique words from the corpus. All words are in lowercase and their stem forms. The field is sorted by the number of documents that contain words in descending order.Number of Documents Containing the Word: In this content, binary calculation is used: if a word exists in an abstract then there is a count of 1. If the word exits more than once in a document, the count is still 1. Total number of document containing the word is counted as the sum of 1s in the entire corpus.Number of Appearance in Corpus: It contains how many times a word occurs in the corpus when the corpus is considered as one large document.Instructions for R CodeLScD_Creation.R is an R script for processing the LSC to create an ordered list of words from the corpus [2]. Outputs of the code are saved as RData file and in CSV format. Outputs of the code are:Metadata File: It includes all fields in a document excluding abstracts. Fields are List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.File of Abstracts: It contains all abstracts after pre-processing steps defined in the step 4.DTM: It is the Document Term Matrix constructed from the LSC[6]. Each entry of the matrix is the number of times the word occurs in the corresponding document.LScD: An ordered list of words from LSC as defined in the previous section.The code can be used by:1.Download the folder ‘LSC’, ‘list_of_prefixes.csv’ and ‘list_of_substitution.csv’2.Open LScD_Creation.R script3.Change parameters in the script: replace with the full path of the directory with source files and the full path of the directory to write output files4.Run the full code.References[1]N. Suzen. (2019). LSC (Leicester Scientific Corpus) [Dataset]. Available: https://doi.org/10.25392/leicester.data.9449639.v1[2]N. Suzen. (2019). LScD-LEICESTER SCIENTIFIC DICTIONARY CREATION. Available: https://github.com/neslihansuzen/LScD-LEICESTER-SCIENTIFIC-DICTIONARY-CREATION[3]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4]A. Thomas, "Common Prefixes, Suffixes and Roots," Center for Development and Learning, 2013.[5]C. Ramasubramanian and R. Ramya, "Effective pre-processing activities in text mining using improved porter’s stemming algorithm," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 12, pp. 4536-4538, 2013.[6]I. Feinerer, "Introduction to the tm Package Text Mining in R," Accessible en ligne: https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf, 2013.

PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE, KIRK BORNE, SANDIPAN DEY, SUGANDHA ARORA, AND HILLOL KARGUPTA** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

Global process mining software market is expected to revenue of around USD 41.74 billion by 2032, growing at a CAGR of around 42.86% between 2024-2032.

DATA MINING THE GALAXY ZOO MERGERS STEVEN BAEHR, ARUN VEDACHALAM, KIRK BORNE, AND DANIEL SPONSELLER Abstract. Collisions between pairs of galaxies usually end in the coalescence (merger) of the two galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of most galaxies, including our own Milky Way. With the onset of massive collection of astronomical data, a computerized and automated method will be necessary for identifying those colliding galaxies worthy of more detailed study. This project researches methods to accomplish that goal. Astronomical data from the Sloan Digital Sky Survey (SDSS) and human-provided classifications on merger status from the Galaxy Zoo project are combined and processed with machine learning algorithms. The goal is to determine indicators of merger status based solely on discovering those automated pipeline-generated attributes in the astronomical database that correlate most strongly with the patterns identified through visual inspection by the Galaxy Zoo volunteers. In the end, we aim to provide a new and improved automated procedure for classification of collisions and mergers in future petascale astronomical sky surveys. Both information gain analysis (via the C4.5 decision tree algorithm) and cluster analysis (via the Davies-Bouldin Index) are explored as techniques for finding the strongest correlations between human-identified patterns and existing database attributes. Galaxy attributes measured in the SDSS green waveband images are found to represent the most influential of the attributes for correct classification of collisions and mergers. Only a nominal information gain is noted in this research, however, there is a clear indication of which attributes contribute so that a direction for further study is apparent.