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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Clustering Algorithms: Experiment and Improvements

verfasst von : Anand Khandare, A. S. Alvi

Erschienen in: Computing and Network Sustainability

Verlag: Springer Singapore

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Abstract

Clustering is data mining method to divide the data objects into n number of clusters. Clustering algorithms can be used in domains such as e-commerce, bio-informatics, image segmentation, speech recognition, financial analysis, and fraud detection. There is abandon knowledge in the clustering research and applications and also various improvements are done on various clustering algorithms. This paper includes the study and survey of various concepts and clustering algorithms by experimenting on it on some data sets and then analyzed gaps and scope for enhancement and scalability of algorithms. Then improved k-means is proposed to minimize these gaps. This improved algorithm automatically finds value of number of clusters and calculates initial centroids in better way rather random selection. From the experimentation, it is found that numbers of iterations are reduced; clusters quality increased and also minimized empty clusters in proposed algorithm.

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Vorheriges Kapitel Development of Path Loss Models for Localization and Creation of Wi-Fi Map in a Wireless Mesh Test Bed
Nächstes Kapitel Urban Traffic State Estimation Techniques Using Probe Vehicles: A Review
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Metadaten
Titel
Clustering Algorithms: Experiment and Improvements
verfasst von
Anand Khandare
A. S. Alvi
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computing and Network Sustainability

Print ISBN: 978-981-10-3934-8

Electronic ISBN: 978-981-10-3935-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3935-5_27

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