Abstract
Clustering in data mining is a supreme step toward organizing data into some meaningful patterns. It plays an extremely crucial role in the entire KDD process, and also as categorizing data is one of the most rudimentary steps in knowledge discovery. Clustering is used for creating partitions or clusters of similar objects. It is an unsupervised learning task used for exploratory data analysis to find some unrevealed patterns which are present in data but cannot be categorized clearly. Sets of data can be designated or grouped together based on some common characteristics and termed clusters, and the implementation steps involved in cluster analysis are essentially dependent upon the primary task of keeping objects within a cluster more closer than objects belonging to other groups or clusters. Depending on the data and expected cluster characteristics, there are different types of clustering algorithms. In the very recent times, many new algorithms have emerged, which aim toward bridging the different approaches toward clustering and merging different clustering algorithms given the requirement of handling sequential, high-dimensional data with multiple relationships in many applications across a broad spectrum. The paper aims to survey, study, and analyze few clustering algorithms and provides a comprehensive comparison of their efficiency on some common grounds. This study also contributes in correlating some very important characteristics of an efficient clustering algorithm.
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Bindra, K., Mishra, A., Suryakant (2019). Effective Data Clustering Algorithms. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_39
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DOI: https://doi.org/10.1007/978-981-13-0589-4_39
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