A New Approach On Incremntal Affinity Propagation Clustering Technique Based On Preference
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2015, Vol 4, Issue 9
Abstract
Many of the clustering algorithms were intended for discovering patterns in static data. Nowadays, more and more data e.g., blogs, Web pages, video surveillance, etc., are come into view in dynamic manner, known as datastreams. Consequently, incremental clustering, evolutionary clustering, and data stream clustering are becoming scorching topics in data mining societies. Characteristics of the dynamic data, or data streams, include their high volume and potentially unbounded size, sequential access, and dynamically evolving nature. This impresses additional requirements to traditional clustering algorithms to hastily process and recap the immense amount of ad infinitum arriving data. It also necessitate the aptitude to adapt to changes in the data distribution, the ability to detect emerging clusters and differentiate them from outliers in the data, and the ability to merge old clustersor discard expired ones. All of these needs make lively data clustering an important confront.
Authors and Affiliations
T. V Satyavathi, Dr. A Krishna Mohan
Keywords
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