Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring systems. For example, consider an airline manufacturer [tex]$\mathcal{C}$[/tex] manufacturing an aircraft model [tex]$A$[/tex] and selling it to five different airline operating companies [tex]$\mathcal{V}_1 \dots \mathcal{V}_5$[/tex]. These aircrafts, during their operation, generate huge amount of data. Mining this data can reveal useful information regarding the health and operability of the aircraft which can be useful for disaster management and prediction of efficient operating regimes. Now if the manufacturer [tex]$\mathcal{C}$[/tex] wants to analyze the performance data collected from different aircrafts of model-type [tex]$A$[/tex] belonging to different airlines then central collection of data for subsequent analysis may not be an option. It should be noted that the result of this analysis may be statistically more significant if the data for aircraft model [tex]$A$[/tex] across all companies were available to [tex]$\mathcal{C}$[/tex]. The potential problems arising out of such a data mining scenario are:

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, i.e., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

The Data Mining and Modeling market is experiencing robust growth, driven by the exponential increase in data volume and the rising need for businesses to extract actionable insights for strategic decision-making. The market, estimated at $25 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $75 billion by 2033. This growth is fueled by several key factors, including the increasing adoption of cloud-based data mining solutions, the development of sophisticated analytical tools capable of handling big data, and the growing demand for predictive analytics across diverse sectors such as finance, healthcare, and retail. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the capabilities of data mining and modeling tools, enabling more accurate predictions and deeper insights. The market is segmented by various deployment models (cloud, on-premise), analytical techniques (regression, classification, clustering), and industry verticals. The major restraints on market growth include the high cost of implementation and maintenance of data mining and modeling solutions, the scarcity of skilled professionals proficient in advanced analytical techniques, and concerns about data privacy and security. However, these challenges are being gradually addressed through the development of user-friendly tools, the emergence of specialized training programs, and the increasing adoption of robust security measures. The competitive landscape is characterized by a mix of established players like SAS and IBM, along with several specialized providers like Symbrium, Coheris, and Expert System. These companies are constantly innovating to enhance their offerings and cater to the evolving needs of businesses across various industries. The market's trajectory indicates a promising future driven by ongoing technological advancements and the increasing importance of data-driven decision-making in a rapidly evolving business environment.

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.

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.

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, i.e., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

The global market for data mining tools is experiencing robust growth, projected to reach $882.8 million in 2025. While the provided CAGR is missing, considering the rapid advancements in artificial intelligence, machine learning, and big data analytics, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 15%. This signifies a significant expansion of the market, driven by the increasing need for businesses to extract valuable insights from massive datasets for improved decision-making, enhanced operational efficiency, and competitive advantage. Key drivers include the rising adoption of cloud-based data mining solutions, the proliferation of big data, and growing investments in advanced analytics capabilities across various sectors like healthcare, finance, and retail. Furthermore, the continuous development of sophisticated algorithms and user-friendly interfaces is making data mining accessible to a wider range of users, fueling market growth. The market is highly competitive, with established players like IBM, SAS Institute, Oracle, and Microsoft alongside emerging innovative companies like H2O.ai and Dataiku vying for market share. The segmentation of the market is diverse, encompassing various deployment models (cloud, on-premise), application types (predictive modeling, customer segmentation, fraud detection), and industry verticals. While restraints such as the high cost of implementation and the need for specialized skills can hinder wider adoption, the overall market outlook remains positive. The predicted CAGR of 15% suggests the market will likely exceed $3 billion by 2033, driven by continued technological innovation, increasing data volumes, and the growing recognition of data mining's crucial role in achieving business success in an increasingly data-driven world.

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its k-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.

Feature selection is an important technique for data mining before a machine learning algorithm is applied. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features can be used for prediction. We attempt to tackle this challenge by studying sparsity regularization and truncation techniques. Specifically, this article addresses two different tasks of online feature selection: (1) learning with full input where an learner is allowed to access all the features to decide the subset of active features, and (2) learning with partial input where only a limited number of features is allowed to be accessed for each instance by the learner. We present novel algorithms to solve each of the two problems and give their performance analysis. We evaluate the performance of the proposed algorithms for online feature selection on several public datasets, and demonstrate their applications to real-world problems including image classification in computer vision and microarray gene expression analysis in bioinformatics. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques.Related Publication: Hoi, S. C., Wang, J., Zhao, P., & Jin, R. (2012). Online feature selection for mining big data. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (pp. 93-100). ACM. http://dx.doi.org/10.1145/2351316.2351329 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2402/ Wang, J., Zhao, P., Hoi, S. C., & Jin, R. (2014). Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. http://dx.doi.org/10.1109/TKDE.2013.32 Full text available in InK: http://ink.library.smu.edu.sg/sis_research/2277/

We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

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.

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.

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 corpus provides the simulation data mining community with a collection of 14641 bridge models and simulated behavior.

1. Folder "1-designs":

The text files in this directory should contain all information for the

independent variables any machine learning experiment. For reference, all 14641 IFC models are supplied in subfolders 001 to 147.

2. Folder "2-simulation":

This folder contains samples of the simulation output that may be viewed in Paraview (http://www.paraview.org). The original model contains the "Org" filename fragment, and the maximum and minimum behaviors are indicated with "Max" and "Min" filename fragments. Displacement, strain, and stress behaviors are all given. Only three of the 14641 models are given as the file sizes are around 1.4 to 2.2 megabytes each. The complete data (approximately 81 gigabytes) can be regenerated and provided if necessary on request (email webis@medien.uni-weimar.de).

3. Folder "3-aggregation":

Maximum displacement, strain, and stress measurements are given in the text files individually, and together in the files with the "vtk" filename fragment. This data should be sufficient for the dependent variables of any machine learning experiment.

The Data Mining Software market is experiencing robust growth, driven by the increasing need for businesses to extract actionable insights from massive datasets. The market's expansion is fueled by several key factors: the proliferation of big data, advancements in machine learning algorithms, and the growing adoption of cloud-based data analytics solutions. Businesses across various sectors, including finance, healthcare, and retail, are leveraging data mining software to improve operational efficiency, enhance customer experience, and gain a competitive edge. The market is segmented by software type (e.g., predictive analytics, text mining, etc.), deployment model (cloud, on-premise), and industry vertical. While the competitive landscape is crowded with both established players like SAS and IBM, and emerging niche providers, the market is expected to consolidate somewhat as larger companies acquire smaller, specialized firms. This consolidation will likely lead to more integrated and comprehensive data mining solutions. The projected Compound Annual Growth Rate (CAGR) suggests a significant increase in market size over the forecast period (2025-2033). While precise figures are unavailable, assuming a conservative CAGR of 15% and a 2025 market size of $5 billion (a reasonable estimate given the size and growth of related markets), we can project substantial growth. Challenges remain, however, including the need for skilled data scientists to manage and interpret the results, as well as concerns about data security and privacy. Addressing these challenges will be crucial for continued market expansion. The increasing availability of open-source tools also presents a challenge to established vendors, demanding innovation and competitive pricing strategies.

Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified. Schumacher and Hall model and ANN showed the best results for volume estimation as function of dap and height. Machine learning methods were more accurate than the Hradetzky polynomial for tree form estimations. ML models have proven to be appropriate as an alternative to traditional modeling applications in forestry measurement, however, its application must be careful because fit-based overtraining is likely.

ABSTRACT This paper presents the application of data mining techniques for pattern identification obtained from the analysis of meteorological variables and their correlation with the occurrence of intense rainfall. The used data were collected between 2008 and 2012 by the surface meteorological station of the Polytechnic Institute of Rio de Janeiro State University, located in Nova Friburgo - RJ, Brazil. The main objective is the automatic prediction related to extreme precipitation events surrounding the meteorological station location one hour prior its occurrence. Classification models were developed based on decision trees and artificial neural networks. The steps of consistency analysis, treatment and data conversion, as well as the computational models used are described, and some metrics are compared in order to identify their effectiveness. The results obtained for the most accurate model presented a rate of 82. 9% of hits related to the prediction of rainfall equal to or greater than 10 mm h-1 one hour prior its occurrence. The results indicate the possibility of using this work to predict risk events in the study region.

Question Paper Solutions of chapter Model assessment measures of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Assess risk in publically traded companies with LSEG's StarMine Text Mining Credit Risk Model (TMCR), scoring over 38,000 companies.

Churn prediction aims to detect customers intended to leave a service provider. Retaining one customer costs an organization from 5 to 10 times than gaining a new one. Predictive models can provide correct identification of possible churners in the near future in order to provide a retention solution. This paper presents a new prediction model based on Data Mining (DM) techniques. The proposed model is composed of six steps which are; identify problem domain, data selection, investigate data set, classification, clustering and knowledge usage. A data set with 23 attributes and 5000 instances is used. 4000 instances used for training the model and 1000 instances used as a testing set. The predicted churners are clustered into 3 categories in case of using in a retention strategy. The data mining techniques used in this paper are Decision Tree, Support Vector Machine and Neural Network throughout an open source software name WEKA.