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Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review

M Sankara Prasanna Kumar, A P. Siva Kumar, K Prasanna
2018 International Journal of Engineering & Technology  
Concept drift is defined as the distributed data across multiple data streams that change over the time.  ...  The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams.  ...  Conclusion The manuscript endeavored to insight the concept drift and its impact on mining models over data streams.  ... 
doi:10.14419/ijet.v7i3.6.14959 fatcat:5tntlnlysngutcf3v2g4kg4bl4

Data stream mining techniques: a review

Eiman Alothali, Hany Alashwal, Saad Harous
2019 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining.  ...  In this paper, we review real time clustering and classification mining techniques for data stream.  ...  KME [33] is a recent classifier leverages supervised and unsupervised knowledge to detect concept drift and recognizes recurrent concepts.  ... 
doi:10.12928/telkomnika.v17i2.11752 fatcat:rls2qzcl3vhobmkpycsdwhzplu

2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32

2021 IEEE Transactions on Knowledge and Data Engineering  
., +, TKDE Dec. 2020 2438-2452 Minimax techniques Generative Adversarial Active Learning for Unsupervised Outlier Detection.  ...  Khan, I., +, TKDE Sept. 2020 1838-1853 G Game theory Generative Adversarial Active Learning for Unsupervised Outlier Detection.  ... 
doi:10.1109/tkde.2020.3038549 fatcat:75f5fmdrpjcwrasjylewyivtmu

Machine Learning [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
For example, ML systems can be trained on automatic speech recognition systems (such as iPhone's Siri) to convert acoustic information in a sequence of speech data into semantic structure expressed in  ...  Key Terminology To facilitate the reader's understanding of the concept of ML, this section defines and discusses some key multidisciplinary conceptual terms in relation to ML.  ...  But, technical challenges prevent us from computing models over large amounts streaming data in the presence of environment drift and concept drift.  ... 
doi:10.1007/978-1-4302-5990-9_1 fatcat:5hwjpdcxb5ctlavhtw3iql2oei

A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams

Saad Mohamad, Abdelhamid Bouchachia, Moamar Sayed-Mouchaweh
2018 IEEE Transactions on Neural Networks and Learning Systems  
Index Terms-Active learning (AL), Bayesian online learning, concept drift, data streams. 2162-237X  ...  The challenge for streaming is that the data distribution may evolve over time, and therefore the model must adapt.  ...  Here, v t can be thought of as the Bayesian version of the window size in batch learning for data stream.  ... 
doi:10.1109/tnnls.2016.2614393 pmid:27775910 fatcat:4bj6vywl3fhz7h7lpzg7gw2l6a

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo-Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, Zahid Usman
2019 Future generations computer systems  
The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports  ...  To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads  ...  Data Stream Analytics in CPS Mining data streams, acquired from various sensors and other monitoring components embedded in the physical systems, plays an essentially role in CPS, as it extracts the insights  ... 
doi:10.1016/j.future.2018.06.042 fatcat:kj722esur5g5vinajrdanunjw4

Application of Machine Learning Approaches in Intrusion Detection System: A Survey

Nutan Farah, Md. Avishek, Faisal Muhammad, Abdur Rahman, Musharrat Rafni, Dewan Md.
2015 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems this survey paper enlisted the 49 related studies in the  ...  Over the past years, many studies have been conducted on the intrusion detection system.  ...  Sivatha Sindhu, 2012)[48]  Neural ensemble decision tree DR : 98.38 44 2013 An adaptive ensemble classifier for mining concept drifting data streams (Dewan Md. Farid L.  ... 
doi:10.14569/ijarai.2015.040302 fatcat:v7a66oibcrhczn47azmlnl4q3q

2019 Index IEEE Transactions on Knowledge and Data Engineering Vol. 31

2020 IEEE Transactions on Knowledge and Data Engineering  
Broneske, D., +, TKDE July 2019 1296-1311 Enhanced Clients for Data Stores and Cloud Services. Iyengar, A., TKDE Oct. 2019 1969-1983 Cameras Learning under Concept Drift: A Review.  ...  ., +, TKDE Dec. 2019 2423-2440 Data models Correlated Matrix Factorization for Recommendation with Implicit Feedback. He, Y., +, TKDE March 2019 451-464 Learning under Concept Drift: A Review.  ... 
doi:10.1109/tkde.2019.2953412 fatcat:jkmpnsjcf5a3bhhf4ian66mj5y

Open challenges for data stream mining research

Georg Krempl, Myra Spiliopoulou, Jerzy Stefanowski, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi
2014 SIGKDD Explorations  
This article presents a discussion on eight open challenges for data stream mining.  ...  of complex data, and evaluation of stream mining algorithms.  ...  on the challenges in stream mining.  ... 
doi:10.1145/2674026.2674028 fatcat:y3bozzeohveibgxb5wmiwfcogm

Stream-based active learning for sliding windows under the influence of verification latency

Tuan Pham, Daniel Kottke, Georg Krempl, Bernhard Sick
2021 Machine Learning  
Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.  ...  In this article, we propose to simulate the available data at the time when the label would arrive.  ...  Furthermore, we thank the anonymous reviewers for their helpful comments and suggestions. Funding Open Access funding enabled and organized by Projekt DEAL.  ... 
doi:10.1007/s10994-021-06099-z fatcat:xngntkzvqvgz7lvybrcac7izr4

A survey of methods for time series change point detection

Samaneh Aminikhanghahi, Diane J. Cook
2016 Knowledge and Information Systems  
Definition 1: A time series data stream is an infinite sequence of elements where x i is a d-dimensional data vector arriving at time stamp i [17].  ...  Finally, we present some grand challenges for the community to consider.  ...  Finally, an ongoing challenge for CPD is to handle non-stationary time series. Literature does exist for detecting concept drift, which can be utilized to help with this issue [69] [70] .  ... 
doi:10.1007/s10115-016-0987-z pmid:28603327 pmcid:PMC5464762 fatcat:qtjvqsgdkjgwtivwlmjn27xyde

Sentiment Analysis [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Be able to react to concept drift in case of evolving data streams.  ...  Additionally, streams classifiers have to adapt to concept drifts.  ...  We define another operation on case statements max 9E x that is crucial for SDP.  ... 
doi:10.1007/978-1-4899-7687-1_100512 fatcat:ce4yyqo2czftzcx2kbauglh3fu

Spike-Timing-Dependent Plasticity [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Be able to react to concept drift in case of evolving data streams.  ...  Additionally, streams classifiers have to adapt to concept drifts.  ...  We define another operation on case statements max 9E x that is crucial for SDP.  ... 
doi:10.1007/978-1-4899-7687-1_774 fatcat:2jprihjaxfbtpb3ttwuuz3u34y

Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data [chapter]

Sreelekha Guggilam, Syed Mohammed Arshad Zaidi, Varun Chandola, Abani K. Patra
2019 Lecture Notes in Computer Science  
The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary thresholds on the anomaly scores or knowledge of the number of clusters while performing  ...  These methods are often tailored to target specific data sets with "known" number of clusters.  ...  Acknowledgements The authors would like to acknowledge University at Buffalo Center for Computational Research (http://www.buffalo.edu/ccr.html) for its computing resources that were made available for  ... 
doi:10.1007/978-3-030-22747-0_4 fatcat:c27gmzkpkrdmniwwpekwisz4b4

A probability theoretic approach to drifting data in continuous time domains [article]

Fabian Hinder, André Artelt, Barbara Hammer
2019 arXiv   pre-print
Further, it induces a technology, to decompose observed data into a drifting and a non-drifting part.  ...  This particularly intuitive formalization enables us to design a new, efficient drift detection method.  ...  Acknowledgement Funding by the VW Foundation in the frame of the project IMPACT, and by the BMBF for the project ITS ML, grant number 01IS18041, is gratefully acknowledged.  ... 
arXiv:1912.01969v1 fatcat:j53mz4pzbvh3je2wgqlmgnvl74
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