i-manager Publications

Subspace Clustering on High Dimensional Data

0*, P. Venkateswarlu Reddy**

* PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Periodicity:May - July'2016
DOI : https://doi.org/10.26634/jcc.3.3.8297

Abstract

Due to the increase of large amount of real world, it is a difficult task for the organizations, companies, etc., to extract relevant data from large amounts of necessary and unnecessary data. Many researches will be going on from the few decades onwards. In datamining there is a concept called clustering which will be used for the smaller datasets, where it effectively makes relevant data into clusters. But the problem will arise on the larger datasets, where it will face a complexity for grouping relevant data into cluster. In this paper, analysis have been referred from many of the algorithms like subscale algorithm for finding the dense region from the dataset and DBscan algorithm for making a cluster as a result it takes dataset as an input and scans a complete dataset. The problem occurs on the time complexity and performance and also, it will follow a sequential flow of database scan. So, it takes time for relevant data values as a cluster in final result. In this analysis, it will allow the complete dataset scans at a time, processing data in parallel manner. So, the resultant data is in effective manner in a lesser time. For the improvement of previous algorithm a Map Red based DBscan for reducing time complexity and performance improvement has been used.

Keywords

Big Data Mining, High Dimensional Data, Subspace Clustering, Scalable Data Mining

How to Cite this Article?

Bhargav, M., and Reddy, P. V. (2016). Subspace Clustering On High Dimensional Data. i-manager’s Journal on Cloud Computing, 3(3), 18-26. https://doi.org/10.26634/jcc.3.3.8297

References

[1]. Elhamifar E., and Vidal R., (2013). “Sparse subspace clustering: Algorithm, theory, and applications”. IEEE Trans. Pattern Anal Mach Intell., Vol. 35, No. 11, pp. 2765- 2781.

[2]. Günnemann S., Boden B., and Seidl T., (2012). “Finding density-based subspace clusters in graphs with feature vectors”. In: Data Mining and Knowledge Discovery, Springer, US, Vol. 25. pp. 243-269.

[3]. Vidal R., (2011). “Subspace clustering”. IEEE Signal Proc Mag, Vol. 28, No. 2, pp. 52–68.

[4]. Kriegel HP., Kröger P., Zimek A., and Oger PKR, (2009). “Clustering high-dimensional data: A survey on subspace clustering, pattern based clustering, and correlation clustering”. ACM Trans Knowl Discov Data, Vol. 3, No. 1, pp. 1–58.

[5]. Yoon S., Nardini C., Benini L., and De Michelin G., (2005). “Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams”. IEEE/ACM Trans Comput BiolBioinforma, Vol. 2, No. 4, pp. 33.

[6]. Xu R., and Wunsch D., (2005). “Survey of clustering algorithms”. Neural Netw IEEE Trans., Vol. 16, No. 3, pp. 645-678.

[7]. Jiang D., Tang C., and Zhang A., (2004). “Cluster analysis for gene expression data: A survey”. IEEE Trans Knowl Data Eng, Vol. 16, No. 11, pp. 1370–1386.

[8]. Parsons L., Haque E., and Liu H., (2004). “Subspace clustering for highdimensional data: A review”. ACM SIGKDD Explor Newspp, Vol. 6, No. 1, pp. 9105.

[9]. Steinbach M., Ertoz L., and Kumar V., (2004). “The challenges of clustering high dimensional data”. In: New Directions in Statistical Physics, Springer, Berlin Heidelberg, pp.273–309.

Subspace Clustering on High Dimensional Data

Abstract

Keywords

How to Cite this Article?

References

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