AN ACTIVE RE-CLUSTER BASED ON SELECTION PROCESSING ALGORITHM

Author (s) : J.Jayapriya, J.Gnana mano sheebha, K.Karnan

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Abstract :

Clustering is a challenging issue in data streams domain. This is because the large volume of data arriving in a stream and evolving over time. Several clustering algorithms have been developed for budding data streams. Besides inadequate memory, the nature of evolving data stream implies some requirements for clustering.This paper analyzes the requirements needed for clustering evolving data streams. We review some of the latest algorithms in the literature and discuss how they meet the requirements. We also discuss the use of feature selection in clustering evolving data streams. Feature selection is a technique that can be used to improve the accuracy of clustering algorithms by reducing the dimensionality of the data.We propose a new active re-cluster based on selection processing algorithm for clustering evolving data streams. Our algorithm is based on the Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm. We evaluate our algorithm on a number of real-world data sets and show that it outperforms other clustering algorithms for evolving data streams.

Keywords : clustering, data streams, feature selection, mRMR, active re-cluster

Conference Date : 13/11/2020

Pages : 181

Paper ID : 20181