International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Clustering Algorithms for Data Stream Karishma Nadhe1, Prof. P. M. Chawan2 1Student,
Dept of CS & IT, VJTI Mumbai, Maharashtra, India
2Professor,
Dept of CS & IT, VJTI Mumbai, Maharashtra, India
Abstract: Nowadays streaming data is delivered by more and more applications, due to this crucial method for data and knowledge engineering is considered to be clustering data streams. It is a two step process. A normal approach is to summarize the data stream in real-time with an online process into so called micro-clusters. Local density estimates are represented by micro-clusters by assembling the information of many data points which is defined in an area. A traditional clustering algorithm is used in a second offline step, in which larger final clusters are formed by reclustering the microclusters. For reclustering, the pseudo points which are used are actually coordinator of the micro-clusters with the weights which are density estimates. However, in the online process, information about density in the area between micro-clusters is not preserved and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). In this paper we depicted various algorithms that are used for clustering and their workings in brief.
Keywords: Data mining, data stream clustering, density-based clustering, micro-cluster, reclustering. 1. Introduction Data mining is basically used to extract useful information from large sets of data. Clustering is the commonly utilized data mining strategy. In this process, the classification of objects is done into different groups by partitioning sets of data into a series of subsets or clusters. Detecting possible intrusions and also tracking the network data utilized to identify changes in traffic patterns is the illustration of clustering. An additional requirement for this case is, as data is produced it must be processed. The sequence of data points is ordered and unbounded in data stream[1],[2]. For applications like GPS data from smart phones, web click-stream data, computer network monitoring data, telecommunication connection data, readings from sensor nets, stock quotes, etc. such data streams are generated. The reclustering approaches join micro-clusters which are separated by a small area of low density and also are close together. These approaches completely ignore the data density in the area between the micro-clusters. To solve this problem, Tu and Chen introduced an extension to the grid-based D-Stream algorithm based on the concept of attraction between adjacent grids cells and showed its effectiveness. And to solve this problem for micro- cluster-based algorithms we have discussed an algorithm. Here the concept of a shared density graph has been introduced which explicitly captures the density of the original data between micro-
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