Abstract
Clustering is an unsupervised learning process of grouping a set of objects into classes of similar objects. Hierarchical method of clustering is an important data mining technique. In this paper we propose hierarchical clustering of projected data stream objects. Cluster Validity Index is used to accurately identify the desired number of clusters present in data set. Thus the user does not need to have prior knowledge about the number of classes present in given data stream. A multi-dimensional grid data structure is maintained, where the received data stream objects are projected. Using a fading function the data objects present in certain time limits are maintained, rest are discarded as time advances. Hierarchical clustering is then performed on this projected grid structure which gives the real clusters present in the given data stream at that instant of time. The proposed algorithm is fast enough to cope-up with the high speed stream as it just needs to find the connected cells present in the grid structure to discover clusters. Experiments performed on the synthetically generated data stream at the rate of 1000 records per second show that the results obtained reflect the actual cluster present.
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Pardeshi, B., Toshniwal, D. (2011). Hierarchical Clustering of Projected Data Streams Using Cluster Validity Index. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. CCSIT 2011. Communications in Computer and Information Science, vol 131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17857-3_54
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DOI: https://doi.org/10.1007/978-3-642-17857-3_54
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