Improvements in Clustering Using Affinity Propagation: A Review
Aniket Bombatkar, Thaksen Parvat
Journal of Information & Systems Management, Vol. 5, No. 2, 2015.
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Abstract:
Data Analysis is getting more and more important in Todayâ€™s world. If data of a person gets lost then, it will be a loss of his/her identity so maintain a big or huge amount of data without loosing, is a challenge. Data clustering can do that because it can maintain a huge amount of data by dividing it into various clusters.
Clustering is separation of similar data from dissimilar content. This paper reviews the data clustering techniques used to separate large amount of data. Motivation for this paper comes from an enormous amount of data in clusters and finding a small errors in them. The review shows that Incremental clustering is becoming a significant problem because the data is
generating in an enormous amount. The data can be of various types like it could be in the form of text, spatial data, images,
sequence data, data in the form of streams, multimedia data. To manage such kinds of data, data mining has various
techniques. Affinity Propagation (AP) is one of the methods that has use in much incremental clustering problems. Most
recent approaches reduce the data content by various methods such as compressed model that combines horizontal compression
with vertical compression. The review covers incremental clustering, Affinity propagation to maintain a large amount of
data and other methods regarding clustering. The analysis shows a trend towards reducing a data into small size so the data
analyzation will become accessible. Data is information and to take out the knowledge from that information is important, so
that mining of data will become efficient.