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.

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.

Subject Area: Text Mining Description: This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining algorithms for document classification. The documents in question were aviation safety reports that documented one or more problems that occurred during certain flights. The goal was to label the documents with respect to the types of problems that were described. This is a subset of the Aviation Safety Reporting System (ASRS) dataset, which is publicly available. How Data Was Acquired: The data for this competition came from human generated reports on incidents that occurred during a flight. Sample Rates, Parameter Description, and Format: There is one document per incident. The datasets are in raw text format. All documents for each set will be contained in a single file. Each row in this file corresponds to a single document. The first characters on each line of the file are the document number and a tilde separats the document number from the text itself. Anomalies/Faults: This is a document category classification problem.

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023

This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.

The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).

## Data sources

Folder 01_SourceData/

- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)

- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)

- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)

- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)

## Automatic classification

Folder 02_AutomaticClassification/

- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title)

- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)

- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)

- oddpub_results_wDOIs.csv (results file of the ODDPub classification)

- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)

## Manual coding

Folder 03_ManualCheck/

- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)

- ManualCheck_2023-06-08.csv (Manual coding results file)

- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)

## Explorative analysis for the discoverability of open data

Folder04_FurtherAnalyses

Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German

## R-Script

Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)

Image classification features and examples of statistical results for the data mining approach using a one-versus-one strategy to implement a SVM (support vector machine) multi-class classifier. Data published in: Fefilatyev, S., K. Kramer, L. Hall, D. Goldgof, R. Kasturi, A. Remsen, K. Daly. 2011. Detection of Anomalous Particles from the Deepwater Horizon Oil Spill Using the SIPPER3 Underwater Imaging Platform. Proceedings of International Conference on Data Mining Workshops, p. 741-748. Awarded Data Mining Practice Prize at the IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, December 11-14, 2011. DOI 10.1109/ICDMW.2011.65.

This dataset contains Zenodo's published open access records' metadata, including also records that have been marked by the Zenodo staff as spam and deleted.

The dataset is a gzipped compressed JSON-lines file, where each line is a JSON object representation of a Zenodo record.

Each object contains the terms:
part_of, thesis, description, doi, meeting, imprint, references, recid, alternate_identifiers, resource_type, journal, related_identifiers, title, subjects, notes, creators, communities, access_right, keywords, contributors, publication_date

which are corresponding to the fields with the same name available in Zenodo's record JSON Schema at https://zenodo.org/schemas/records/record-v1.0.0.json.

In addition, some terms have been altered:

The term files contains a list of dictionaries containing filetype, size, and filename only.
The term license contains a short Zenodo ID of the license (e.g "cc-by").
The term spam contains a boolean value, determining whether a given record was marked as a spam record by Zenodo staff.

Some values for the top-level terms, which were missing in the metadata may contain a null value.

A smaller uncompressed random sample of 200 JSON lines is also included to allow for testing and getting familiar with the format without having to download the entire dataset.

Evaluation metrics are presented for the test sample.

From Dryad entry:

"Abstract
Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomic sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and five site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomic sites have convergent miRNA expression programs, likely reflecting morphologic and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multi-layer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histologic diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.

Methods
Sequencing-based miRNA expression profiles from 378 clinical samples, comprising 239 neuroendocrine neoplasm (NEN) cases and 139 site-matched non-NEN controls, were used in this study. Expression profiles were either compiled from published studies (n=149) or generated through small RNA sequencing (n=229). Prior to sequencing, total RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue blocks or fresh-frozen (FF) tissue samples. Small RNA cDNA libraries were sequenced on HiSeq 2500 Illumina platforms using an established small RNA sequencing (Hafner et al., 2012 Methods) and sequence annotation pipeline (Brown et al., 2013 Front Genet) to generate miRNA expression profiles. Scaling our existing approach to miRNA-based NEN classification (Panarelli et al., 2019 Endocr Relat Cancer; Ren et al., 2017 Oncotarget), we constructed and cross-validated a multi-layer classifier for discriminating NEN pathological types based on selected miRNAs.

Usage notes
Diagnostic histopathology and small RNA cDNA library preparation information for all samples are presented in Table S1 of the associated manuscript."

AG News (News articles)

News Articles Text Classification

Source

Huggingface Hub: link

About this dataset

The ag_news dataset provides a new opportunity for text classification research. It is a large dataset consisting of a training set of 10,000 examples and a test set of 5,000 examples. The examples are split evenly into two classes: positive and negative. This makes the dataset well-suited for research into text classification methods

How to use the dataset

If you're looking to do text classification research, the ag_news dataset is a great new dataset to use. It consists of a training set of 10,000 examples and a test set of 5,000 examples, split evenly between positive and negative class labels. The data is well-balanced and should be suitable for many different text classification tasks

Research Ideas

• This dataset can be used to train a text classifier to automatically categorize news articles into positive and negative categories.
• This dataset can be used to develop a system that can identify positive and negative sentiment in news articles.
• This dataset can be used to study the difference in how positive and negative news is reported by different media outlets

Acknowledgements

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine that has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), XML, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:-----------------------------------------| | text | The text of the news article. (string) | | label | The label of the news article. (integer) |

File: test.csv | Column name | Description | |:--------------|:-----------------------------------------| | text | The text of the news article. (string) | | label | The label of the news article. (integer) |

A database of de-identified supermarket customer transactions. This large simulated dataset was created based on a real data sample.

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ag_news_subset', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Context

The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, refer to [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.

Content

For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (score between 0 and 10)

Acknowledgements

This dataset is also available from the UCI machine learning repository, https://archive.ics.uci.edu/ml/datasets/wine+quality, to get both the dataset i.e. red and white vinho verde wine samples, from the north of Portugal, please visit the above link.

Please include this citation if you plan to use this database:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Inspiration

We kagglers can apply several machine-learning algorithms to determine which physiochemical properties make a wine 'good'!

Relevant papers

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Accuracy of financial risk problem with five risk classes (Example 1) using the REM imputation method.

Performance of the “Training Data Set” using the classification algorithm J48.

According to Cognitive Market Research, the global Data Mining Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Focus on Customer Satisfaction to Drive Data Mining Software Market Growth

In today’s hyper-competitive and digitally connected marketplace, customer satisfaction has emerged as a critical factor for business sustainability and growth. The growing focus on enhancing customer satisfaction is proving to be a significant driver in the expansion of the data mining software market. Organizations are increasingly leveraging data mining tools to sift through vast volumes of customer data—ranging from transactional records and website activity to social media engagement and call center logs—to uncover insights that directly influence customer experience strategies. Data mining software empowers companies to analyze customer behavior patterns, identify dissatisfaction triggers, and predict future preferences. Through techniques such as classification, clustering, and association rule mining, businesses can break down large datasets to understand what customers want, what they are likely to purchase next, and how they feel about the brand. These insights not only help in refining customer service but also in shaping product development, pricing strategies, and promotional campaigns. For instance, Netflix uses data mining to recommend personalized content by analyzing a user's viewing history, ratings, and preferences. This has led to increased user engagement and retention, highlighting how a deep understanding of customer preferences—made possible through data mining—can translate into competitive advantage. Moreover, companies are increasingly using these tools to create highly targeted and customer-specific marketing campaigns. By mining data from e-commerce transactions, browsing behavior, and demographic profiles, brands can tailor their offerings and communications to suit individual customer segments. For Instance Amazon continuously mines customer purchasing and browsing data to deliver personalized product recommendations, tailored promotions, and timely follow-ups. This not only enhances customer satisfaction but also significantly boosts conversion rates and average order value. According to a report by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and lift sales by 10% or more—a powerful incentive for companies to adopt data mining software as part of their customer experience toolkit. (Source: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/personalizing-at-scale#/) The utility of data mining tools extends beyond e-commerce and streaming platforms. In the banking and financial services industry, for example, institutions use data mining to analyze customer feedback, call center transcripts, and usage data to detect pain points and improve service delivery. Bank of America, for instance, utilizes data mining and predictive analytics to monitor customer interactions and provide proactive service suggestions or fraud alerts, significantly improving user satisfaction and trust. (Source: https://futuredigitalfinance.wbresearch.com/blog/bank-of-americas-erica-client-interactions-future-ai-in-banking) Similarly, telecom companies like Vodafone use data mining to understand customer churn behavior and implement retention strategies based on insights drawn from service usage patterns and complaint histories. In addition to p...

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

This dataset is text data related to cognitive distortion sentences that are closely related to thought disorder. This is the first dataset of cognitive distortion sentences in Indonesian. This dataset is a collection of distortion/non-distortion sentences generated from online questionnaire answers. The questions are compiled by experts in this case a psychologist. Annotation is also done by experts to obtain distortion classes. The distribution of existing cognitive distortion classes is adjusted to the theory of Burns, D.D. (1999) in the book "The Feeling Good Handbook". The total generated sentence data is 4662, there are complete sentences and parts of sentences that are distortion parts flanked by the "$" sign, along with labels from two annotators in separate columns. Several distortion classes with a limited number of samples were augmented using the back-translation method. The four augmented classes are "Mental Filter," "All-or-Nothing Thinking," "Magnification or Minimization," and "Emotional Reasoning." Each class was expanded to a total of 200 samples. The back-translation process utilized five languages: Chinese (ZH), English (EN), Javanese (JV), Malay (MS), and Tagalog (TG). In the accompanying CSV file, the "DATA STATUS" column indicates the origin of each sentence. Entries labeled "ORI-RAW" refer to raw data collected directly from questionnaire responses. Entries labeled "DIS-[...]" represent distortion sentences generated through back-translation using the five language codes (ZH, EN, JV, MS, and TG). Apart from Indonesian, an English version is also available.

📊 Calling all data aficionados! 🚀 Just stumbled upon some juicy data that might tickle your fancy! If you find it helpful, a little upvote would be most appreciated! 🙌 #DataIsKing #KaggleCommunity 📈

• Data Collection:

  • Collected by members of the WISDM (Wireless Sensor Data Mining) Lab at Fordham University.
  • Utilized accelerometer and gyroscope sensors from smartphones and smartwatches.
  • 51 subjects participated in performing 18 diverse activities of daily living.
  • Each activity was performed for 3 minutes per subject, resulting in 54 minutes of data per subject.
  • Activities encompassed basic ambulation-related tasks, hand-based activities of daily living, and eating activities.

• Activity Categories:

  • Basic ambulation-related activities: walking, jogging, climbing stairs.
  • Hand-based activities of daily living: brushing teeth, folding clothes.
  • Eating activities: eating pasta, eating chips.

• Data Description:

  • Contains low-level time-series sensor data from phone accelerometers, phone gyroscopes, watch accelerometers, and watch gyroscopes.
  • Each time-series data is labeled with the activity being performed and a subject identifier.
  • Suitable for building and evaluating biometric models as well as activity recognition models.

• Data Transformation:

  • Researchers employed a sliding window approach to transform time-series data into labeled examples.
  • Scripts for performing the transformation are provided along with the transformed data.

• Availability:

  • The dataset is accessible from the UCI Machine Learning Repository under the name "WISDM Smartphone and Smartwatch Activity and Biometrics Dataset."

• Dataset Name: WISDM Smartphone and Smartwatch Activity and Biometrics Dataset

• Subjects and Tasks:

  • Data collected from 51 subjects.
  • Each subject performed 18 tasks, with each task lasting 3 minutes.

• Data Collection Setup:

  • Subjects wore a smartwatch on their dominant hand and carried a smartphone in their pocket.
  • A custom app controlled data collection on both devices.
  • Sensors used: accelerometer and gyroscope on both smartphone and smartwatch.

• Sensor Characteristics:

  • Data collected at a rate of 20 Hz (every 50ms).
  • Four total sensors: accelerometer and gyroscope on both smartphone and smartwatch.

• Device Specifications:

  • Smartphone: Google Nexus 5/5X or Samsung Galaxy S5 running Android 6.0 (Marshmallow).
  • Smartwatch: LG G Watch running Android Wear 1.5.

SUMMARY INFORMATION FOR THE DATASET

THE 18 ACTIVITIES REPRESENTED IN THE DATASET

• Non-hand-oriented activities:

  • Walking
  • Jogging
  • Stairs
  • Standing
  • Kicking

• Hand-oriented activities (General):

  • Dribbling
  • Playing catch
  • Typing
  • Writing
  • Clapping
  • Brushing teeth
  • Folding clothes

• Hand-oriented activities (eating):

  • Eating pasta
  • Eating soup
  • Eating sandwich
  • Eating chips
  • Drinking

DEFINITION OF ELEMENTS IN RAW DATA MEASUREMENTS

The set “Example 1” has 10000 observations in each class. In set “Example 2”, the majority and minority classes contain 50400, and 33600 observations, respectively. For details about the data see [8].

This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

This dataset is part of the project with Scotch Whisky Research Institute into detecting forged spirits in a non-intrusive manner. One way of detecting forgery without sampling the wine is through inspecting ethanol level by spectrograph. The dataset covers 20 different bottle types and four levels of alcohol: 35%, 38%, 40% and 45%. Each series is a spectrograph of 1751 observations. This dataset is an example of when it is wrong to merge and resample, because the train/test split are constructed so that the same bottle type is never in both train and test sets. There are 4 classes. - Class 1: E35 - Class 2: E38 - Class 3: E40 - Class 4: E45 For more information about this dataset, see [1,2].

[1] Lines, Jason, Sarah Taylor, and Anthony Bagnall. "Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification." Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016.

[2] J. Large, E. K. Kemsley, N.Wellner, I. Goodall, and A. Bagnall, Detecting forged alcohol non-invasively through vibrational spectroscopy and machine learning," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018.

Donator: A. Bagnall