Incremental clustering of dynamic data streams using connectivity based representative points

Lühr, Sebastian; Lazarescu, Mihai

doi:10.1016/j.datak.2008.08.006

Cited by 76 publications

(31 citation statements)

References 41 publications

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“…Though experimental studies carried out in (Aggarwal et al, 2003;Bhatnagar et al, 2013;Cao et al, 2006;Luhr and Lazarescu, 2009;Park and Lee, 2007) are extensive, they are not oriented towards realworld applications. Careful study of the experimental analysis of these papers reveals that the applications for which stream clustering algorithms are evaluated are somewhat limited, giving rise to doubts whether it is realistic to apply these algorithms in applications, such as weather monitoring, stock trading, telecommunication, web-traffic monitoring etc., as mentioned in these papers 2 .…”

Section: Weak Experimental Evaluationmentioning

confidence: 96%

“…Figure 1 illustrates generic architecture of a clustering algorithm with two components (described in Section 4). There exist some algorithms that are are capable of generating clusters in a single phase (Gao et al, 2005;Luhr and Lazarescu, 2009). However, such algorithms lack ability to handle fast streams.…”

Section: Architecture Of Stream Clustering Algorithmsmentioning

confidence: 99%

“…RepStream algorithm (Luhr and Lazarescu, 2009) is a single phase, graphbased clustering algorithm capable of discovering arbitrary shaped clusters. Graph-based clustering is particularly suitable for modeling of spatio-temporal relationships in data space since it preserves spatial relationship among data points.…”

Section: Repstream Algorithmmentioning

confidence: 99%

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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: Weak Experimental Evaluationmentioning

confidence: 96%

Section: Architecture Of Stream Clustering Algorithmsmentioning

confidence: 99%

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Stream Clustering Algorithms: A Primer

Kaur

Bhatnagar

Chakravarthy

2015

Studies in Big Data

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“…Data stream clustering approaches store and processes large scale data efficiently because it provides summarizations of the past data, see [24,25,26,27,28,29] for more information. In this work seed filling clustering algorithm is utilized to form new data stream algorithm.…”

Section: Growing Process After a Few Iterationsmentioning

confidence: 99%

A Proposed Data Stream Clustering Method for Detecting Anomaly Events in Crowd Scene Surveillance videos

Karim¹,

Shati²

2017

djps

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In this research, a new data stream clustering method utilizing seed based region growing technique is implemented to perform abnormal event detection in anomaly detection system in a new data stream clustering method used in abnormal detection system. This is done by applying HARRIS or FAST detectors on the frames of video clips in two publically available datasets. The first UCSD pedestrian dataset (ped1 and ped2 datasets), and the second VIRAT video dataset system to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the new clustering method. This method in using HARRIS detector achieves detection rates about (9.09%, 52.17%, 61.67%), and the false alarm rates are (18.79%, 36.09%, 66.67%) by using Ped1, Ped2, and VIRAT datasets respectively. For the case of using FAST detector, the bestachieved detection rates are (7.88%, 46.09%, 58.33%), and the false alarms are (21.21%, 40.87%, 63.33%) by using the three previously mentioned benchmarks respectively.

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“…An important but notoriously difficult issue is how to update the clusters when objects are inserted and deleted from the underlying dataset [4,8,15,[17][18][19][20][21]. This is especially true when the clustering problem is mass-correlated, namely, the cluster of an object o cannot be decided by looking Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Density Based Clustering

Gan

Tao

2017

Proceedings of the 2017 ACM International Conference on Management of Data

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Dynamic clustering-how to efficiently maintain data clusters along with updates in the underlying dataset-is a difficult topic. This is especially true for density-based clustering, where objects are aggregated based on transitivity of proximity, under which deciding the cluster(s) of an object may require the inspection of numerous other objects. The phenomenon is unfortunate, given the popular usage of this clustering approach in many applications demanding data updates.Motivated by the above, we investigate the algorithmic principles for dynamic clustering by DBSCAN, a successful representative of density-based clustering, and ρ-approximate DBSCAN, proposed to bring down the computational hardness of the former on static data. Surprisingly, we prove that the ρ-approximate version suffers from the very same hardness when the dataset is fully dynamic, namely, when both insertions and deletions are allowed. We also show that this issue goes away as soon as tiny further relaxation is applied, yet still ensuring the same quality-known as the "sandwich guarantee"-of ρ-approximate DBSCAN. Our algorithms guarantee near-constant update processing, and outperform existing approaches by a factor over two orders of magnitude.

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Incremental clustering of dynamic data streams using connectivity based representative points

Cited by 76 publications

References 41 publications

Stream Clustering Algorithms: A Primer

Stream Clustering Algorithms: A Primer

A Proposed Data Stream Clustering Method for Detecting Anomaly Events in Crowd Scene Surveillance videos

Dynamic Density Based Clustering

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