E-Stream: Evolution-Based Technique for Stream Clustering

Udommanetanakit, Komkrit; Rakthanmanon, Thanawin; Waiyamai, Kitsana

doi:10.1007/978-3-540-73871-8_58

Cited by 88 publications

(62 citation statements)

References 8 publications

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“…require analysis of the temporal changes in the clustering schemes. In the recent past, changes in the clustering schemes have been examined in Kifer et al, 2004;Spillopoulou et al, 2006;Spinosa et al, 2007;Tasoulis et al, 2007;Udommanetanakit et al, 2007). But these are ad hoc and isolated works that need to be consolidated and formalized so as to design a 'change model' for the clustering scheme.…”

Section: Change Modelingmentioning

confidence: 99%

Stream Clustering Algorithms: A Primer

Kaur

Bhatnagar

Chakravarthy

2015

Studies in Big Data

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Abstract.Stream data has become ubiquitous due to advances in acquisition technology and pervades numerous applications. These massive data gathered as continuous flow, are often accompanied by dire need for real-time processing. One aspect of data streams deals with storage management and processing of continuous queries for aggregation. Another significant aspect pertains to discovery and understanding of hidden patterns to derive actionable knowledge using mining approaches. This chapter focuses on stream clustering and presents a primer of clustering algorithms in data stream environment.Clustering of data streams has gained importance because of its ability to capture natural structures from unlabeled, non-stationary data. Single scan of data, bounded memory usage, and capturing data evolution are the key challenges during clustering of streaming data. We elaborate and compare the algorithms on the basis of these constraints. We also propose a taxonomy of algorithms based on the fundamental approaches used for clustering. For each approach, a systematic description of contemporary, well-known algorithms is presented. We place special emphasis on synopsis data structure used for consolidating characteristics of streaming data and feature it as an important issue in design of a stream clustering algorithms. We argue that a number of functional and operational characteristics (e.g. quality of clustering, handling of outliers, number of parameters etc.) of a clustering algorithm are influenced by the choice of synopsis. A summary of clustering features that are supported by different algorithms is given. Finally, research directions for improvement in the usability of stream clustering algorithms are suggested.

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Section: Change Modelingmentioning

confidence: 99%

Stream Clustering Algorithms: A Primer

Kaur

Bhatnagar

Chakravarthy

2015

Studies in Big Data

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“…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 cluster, combining of two similar type of clusters and change in the behavior of cluster data itself. It uses a fading cluster structure with histogram to approximate the streaming data.…”

Section: Data Stream Clustering:-mentioning

confidence: 99%

Data Stream Clustering Issues and Challenges-a Survey.

Rupa¹,

Soujanya²

2017

IJAR

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“…Another point is using a repository for previous data so it is unable to give us a history in different scale time. E-Stream [18] is a data stream clustering technique which supports ISSN: 2231-2803 http://www.ijcttjournal.org…”

Section: IImentioning

confidence: 99%

Performance Comparison of Two Streaming Data Clustering Algorithms

Mahobiya¹,

Kumar²

2014

IJCTT

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Abstract-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. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize center. In this paper we have compared these two methods using KDD-CUP'99 data set. We have compared these algorithms with respect to number of valid clusters, computational time and mean standard error.

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E-Stream: Evolution-Based Technique for Stream Clustering

Cited by 88 publications

References 8 publications

Stream Clustering Algorithms: A Primer

Stream Clustering Algorithms: A Primer

Data Stream Clustering Issues and Challenges-a Survey.

Performance Comparison of Two Streaming Data Clustering Algorithms

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