Unsupervised Clustering In Streaming Data

Tasoulis, Dimitris K.; Adams, Niall M.; Hand, David J.

doi:10.1109/icdmw.2006.165

Cited by 32 publications

(25 citation statements)

References 7 publications

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“…With the incorporation of computational geometry techniques the algorithm achieves a comparatively low time complexity. The algorithm has been successfully applied in numerous applications including bioinformatics [47,48], medical diagnosis [31,49], time series prediction [35] and web personalization [41].…”

Section: Discussionmentioning

confidence: 99%

Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

Tasoulis

Vrahatis

2005

Knowledge Mining

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Summary. The extraction of meaningful information from large collections of data is a fundamental issues in science. To this end, clustering algorithms are typically employed to identify groups (clusters) of similar objects. A critical issue for any clustering algorithm is the determination of the number of clusters present in a dataset. In this contribution we present a clustering algorithm that in addition to partitioning the data into clusters, it approximates the number of clusters during its execution. We further present modifications of this algorithm for different distributed environments, and dynamic databases. Finally, we present a modification of the algorithm that exploits the fractal dimension of the data to partition the dataset.

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Section: Discussionmentioning

confidence: 99%

Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

Tasoulis

Vrahatis

2005

Knowledge Mining

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“…For example, BIRCH (Zhang et al 1996), CluStream (Aggarwal et al 2003, DenStream (Cao et al 2006) or WSTREAM (Tasoulis et al 2006) can be extended to maintain temporal information in form of an EMM.…”

Section: Extensible Markov Modelmentioning

confidence: 99%

rEMM: Extensible Markov Model for Data Stream Clustering inR

Hahsler¹,

Dunham²

2010

J. Stat. Soft.

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Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM) adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the R extension package rEMM which implements EMM and we discuss some examples and applications.

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“…Most approaches to cluster analysis [15] assume that all data is available from the beginning and that the number of clusters is given. Recent work in this area also deals with sequential data and incremental model updates [16,17]. Ghahramani [18] gives an easily accessible overview of the state-of-the-art in unsupervised learning.…”

Section: Related Workmentioning

confidence: 99%

Classifying Dynamic Objects: An Unsupervised Learning Approach

Luber

Arras

Plagemann

et al. 2008

Robotics: Science and Systems IV

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Abstract-For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.

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Unsupervised Clustering In Streaming Data

Cited by 32 publications

References 7 publications

Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

rEMM: Extensible Markov Model for Data Stream Clustering inR

Classifying Dynamic Objects: An Unsupervised Learning Approach

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