A Parameterized Framework for Clustering Streams
Author:
Affiliation:
1. University of Delhi, India
2. I.B.M., Indian Research Lab, India
Abstract
Clustering of data streams finds important applications in tracking evolution of various phenomena in medical, meteorological, astrophysical, seismic studies. Algorithms designed for this purpose are capable of adapting the discovered clustering model to the changes in data characteristics but are not capable of adapting to the user’s requirements themselves. Based on the previous observation, we perform a comparative study of different approaches for existing stream clustering algorithms and present a parameterized architectural framework that exploits nuances of the algorithms. This framework permits the end user to tailor a method to suit his specific application needs. We give a parameterized framework that empowers the end-users of KDD technology to build a clustering model. The framework delivers results as per the user’s application requirements. We also present two assembled algorithms G-kMeans and G-dbscan to instantiate the proposed framework and compare the performance with the existing stream clustering algorithms.
Publisher
IGI Global
Subject
Hardware and Architecture,Software
Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Discovering Similarity Across Heterogeneous Features;International Journal of Data Warehousing and Mining;2020-10
2. Concept Drift Detection in Data Stream Clustering and its Application on Weather Data;International Journal of Agricultural and Environmental Information Systems;2020-01
3. Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away Learning;Journal of Classification;2017-03-20
4. A networked approach to dynamic analysis of social system vulnerability;Journal of Intelligent & Fuzzy Systems;2015
5. Stream Clustering Algorithms: A Primer;Studies in Big Data;2015
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