A Stream Clustering Algorithm Using Information Theoretic Clustering Evaluation Function
No Thumbnail Available
Date
2018
Authors
Gokcay, Erhan
Gökçay, Erhan
Journal Title
Journal ISSN
Volume Title
Publisher
Scitepress
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
There are many stream clustering algorithms that can be divided roughly into density based algorithms and hyper spherical distance based algorithms. Only density based algorithms can detect nonlinear clusters and all algorithms assume that the data stream is an ordered sequence of points. Many algorithms need to receive data in buckets to start processing with online and offline iterations with several passes over the data. In this paper we propose a streaming clustering algorithm using a distance function which can separate highly nonlinear clusters in one pass. The distance function used is based on information theoretic measures and it is called Clustering Evaluation Function. The algorithm can handle data one point at a time and find the correct number of clusters even with highly nonlinear clusters. The data points can arrive in any random order and the number of clusters does not need to be specified. Each point is compared against already discovered clusters and each time clusters are joined or divided using an iteratively updated threshold.
Description
Gokcay, Erhan/0000-0002-4220-199X
ORCID
Keywords
Stream Clustering, Data Stream, Cluster Analysis, Information Theory, Distance Function, Clustering Evaluation Function
Turkish CoHE Thesis Center URL
Fields of Science
Citation
0
WoS Q
Scopus Q
Source
8th International Conference on Cloud Computing and Services Science (CLOSER) -- MAR 19-21, 2018 -- Funchal, PORTUGAL
Volume
Issue
Start Page
582
End Page
588