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E-Stream: Evolution-Based Technique for Stream Clustering [chapter]

Komkrit Udommanetanakit, Thanawin Rakthanmanon, Kitsana Waiyamai
2007 Lecture Notes in Computer Science  
In this paper, we propose a new technique for stream clustering which supports five evolutions that are appearance, disappearance, selfevolution, merge and split.  ...  Stream clustering is a technique that performs cluster analysis of data streams that is able to monitor the results in real time.  ...  Vateekul for their reading and comments of this paper.  ... 
doi:10.1007/978-3-540-73871-8_58 fatcat:qztx5e5b5zb75eyadxdgetx3zu

HUE-Stream: Evolution-Based Clustering Technique for Heterogeneous Data Streams with Uncertainty [chapter]

Wicha Meesuksabai, Thanapat Kangkachit, Kitsana Waiyamai
2011 Lecture Notes in Computer Science  
This paper presented HUE-Stream which extends E-Stream and E-Stream++ by introducing a distance function, cluster representation and histogram management for the different types of clustering structure  ...  The evolution-based stream clustering method supports the monitoring and change detection of clustering structures.  ...  HUE-Stream was extended from the E-Stream (Udommanetanakit et al., 2007) technique and the E-Stream++ (Kosonpothisakun et al., 2009) technique which is an evolution-based stream clustering technique  ... 
doi:10.1007/978-3-642-25856-5_3 fatcat:oxsih76mvzcg3mqoil3qmipao4

E-stream: Towards pattern centric network incident discovery and corrective action recommendation in telecommunication networks

Sebastian Robitzsch, Faisal Zaman, Zhiguo Qu, John Keeney, Sven van der Meer, Gabriel-Miro Muntean
2015 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)  
E-Stream applies dimension reduction, data mining, and recommender system techniques in order to handle very high volumes of management events, identify and predict network incidents, and recommend candidate  ...  This paper presents the architecture of the E-Stream project which aims to support Next Generation Operations Support Systems.  ...  Eventually, E-Stream should be capable of recommending CCAs to domain experts in the NOC by providing a list of suggested CCA, while continuously learning the most suitable CCAs for given patterns based  ... 
doi:10.1109/inm.2015.7140390 dblp:conf/im/RobitzschZQKMM15 fatcat:y4hkua5b2zeyhhiy54s2na2z54

Data stream mining techniques: a review

Eiman Alothali, Hany Alashwal, Saad Harous
2019 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
In this paper, we review real time clustering and classification mining techniques for data stream.  ...  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.  ...  E-Stream [20] and HUE-Stream [21] are evolution-based algorithms based on agglomerative method for clustering data streams.  ... 
doi:10.12928/telkomnika.v17i2.11752 fatcat:rls2qzcl3vhobmkpycsdwhzplu

Constraint-based discriminative dimension selection for high-dimensional stream clustering

Kitsana Waiyamai, Thanapat Kangkachit
2018 IJAIN (International Journal of Advances in Intelligent Informatics)  
SED-Stream is an efficient clustering algorithm that supports high dimension data streams.  ...  Clustering data streams is one of active research topic in data mining.  ...  E-Stream [9] is an evolution-based stream clustering technique that has been developed to detect change of the evolving clustering structures.  ... 
doi:10.26555/ijain.v4i3.271 fatcat:hz5t2fjznzcerpahwh3mxfphsa

A Survey on Clustering Algorithms for Data Streams

Neha Sharma, Shraddha Masih, Pawan Makhija
2018 International Journal of Computer Applications  
Clustering is one of the most useful technique for analsing stream data, as it does not require any predefined class labeling.  ...  Data stream mining is an emerging area for extracting useful information from continuous arriving data.  ...  E-Stream E-Stream is evaluation based technique [7] that has five points: appearance, self evolution, merge, split and disappearance. Initially the data point is considered as isolated cluster.  ... 
doi:10.5120/ijca2018918014 fatcat:pwiw7qjdgrh75lpzykizi3gbgy

Data Stream Partitioning Algorithms for Big Data Analytics: A Review

Hemant Kumar Singh, Vinodani Katiyar
2018 AIMS International Journal of Management  
Various existing mining techniques will not be efficient for such type of streaming data. So these existing data mining techniques need to be enhanced for processing big data streams.  ...  This paper takes a critical review of each of the four types of stream clustering algorithms and concludes with some critical discussions, advantages and disadvantages of each type of algorithm as well  ...  Acknowledgements We would like to offer the sincere gratitude to the management and the University for providing the constant encouragement and support provided throughout the period of this research work  ... 
doi:10.26573/2018.12.2.4 fatcat:zhlnpvzdongmjm3vk6ekfphjpq


B. Rupa, R. Soujanya.
2017 International Journal of Advanced Research  
POD-Clus (Probability and Distribution-based Clustering) [18] is a model based clustering technique for streaming data.  ...  E-Stream [16] is a data stream clustering technique which supports following five type of advancement in streaming data: Appearance of new cluster, Disappearance of an old cluster, Splitting of a large  ... 
doi:10.21474/ijar01/3673 fatcat:m4m2bpum3nhkzlb2jk3kmswdju

Performance Comparison of Two Streaming Data Clustering Algorithms

Chandrakant Mahobiya, Dr. M Kumar
2014 International Journal of Computer Trends and Technology  
The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm  ...  Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration.  ...  E-Stream [18] is a data stream clustering technique which supports ISSN: 2231-2803 Page 57 following five type of evolution in streaming data: Appearance of new cluster, Disappearance  ... 
doi:10.14445/22312803/ijctt-v12p111 fatcat:5b44wrdqwfhqtm6rmu4zi5ezlm

A Survey Paper on Data Stream Mining

Shazia Nousheen M, Dr Prasad G. R
2016 International Journal of Engineering Research and  
The aim of this survey is to provide a brief view on different classification techniques in data mining.  ...  There are various applications of this field hence introduction of new methods for data classification are widely researched to provide better standard of services.  ...  to partition cluster in definite data by using histogram management POD Clus [23] is a model based bunching strategy for streaming information.  ... 
doi:10.17577/ijertv5is080107 fatcat:rdklg2mu5vb4tme2dkjjb3ujd4

A Modified Approach Of Optics Algorithm For Data Streams

M. Shukla, Y. P. Kosta, M. Jayswal
2017 Zenodo  
A mining technique such as clustering is implemented in order to process data streams and generate a set of similar objects as an individual group.  ...  In this paper, a concept of pruning is applied on the stream optics algorithm along with the identification of real outliers, which reduces memory consumption and increases the speed for identifying potential  ...  For cluster extraction it is not supervised technique MR-Stream [9] Density Based Improves the performance of the clustering.  ... 
doi:10.5281/zenodo.571281 fatcat:akmi2a4brzf7jpfnn45fpafyoe

State-of-the-art on clustering data streams

Mohammed Ghesmoune, Mustapha Lebbah, Hanene Azzag
2016 Big Data Analytics  
This paper presents a comprehensive survey of the data stream clustering methods and an overview of the most well-known streaming platforms which implement clustering.  ...  In the literature of data stream clustering methods, a large number of algorithms use a two-phase scheme which consists of an online component that processes data stream points and produces summary statistics  ...  We thank anonymous reviewers for their insightful remarks. Authors' contributions All authors contributed equally. All authors read and approved the final manuscript.  ... 
doi:10.1186/s41044-016-0011-3 fatcat:dimp634rczf7don7jk4tfunvfm

Survey and Research Issues in Data Stream Mining

Lalit Agrawal
2020 Bioscience Biotechnology Research Communications  
In this paper, we present a short survey of different strategies accessible for the information stream mining.  ...  These days, an immense assortment and volume of information is constantly getting created from heterogeneous sources accordingly prompting a huge enthusiasm for the rising field of information stream mining  ...  . few methods for clustering data streams are mentioned below: sTream -guha, mishra, motwani and o'Callaghan proposed a k-median based stream Clustering algorithm.  ... 
doi:10.21786/bbrc/13.14/35 fatcat:5ovcet24srbetkk2gzi4zfvt6i

Growing Hierarchical Trees for Data Stream clustering and visualization

Nhat-Quang Doan, Mohammed Ghesmoune, Hanane Azzag, Mustapha Lebbah
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
Visualization is still a big challenge for large data streams.  ...  The hierarchical component consists of multiple tree-like hierarchic of clusters which allow to describe the evolution of data stream, and then analyze explicitly their similarity.  ...  We thank anonymous reviewers for their insightful remarks.  ... 
doi:10.1109/ijcnn.2015.7280397 dblp:conf/ijcnn/DoanGAL15 fatcat:eiwufiiqvvbr5d3xyhg4zd5dva

Temporal Structure Learning for Clustering Massive Data Streams in Real-Time [chapter]

Michael Hahsler, Margaret H. Dunham
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
In this paper we propose a new framework called Temporal Relationships Among Clusters for Data Streams (TRACDS) which allows us to learn the temporal structure while clustering a data stream.  ...  This framework allows us to preserve temporal relationships among clusters for any state-of-the-art data stream clustering algorithm with only minimal overhead.  ...  E-STREAM [27] adds cluster splitting by maintaining histograms for each cluster and dimension.  ... 
doi:10.1137/1.9781611972818.57 dblp:conf/sdm/HahslerD11 fatcat:qtq3jk7yezg2xlcni4nj2xxeqq
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