Abstract

BIG DATA[1] is a term for data sets that are so large or complex that traditional data processing[4] applications are inadequate. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk. Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method are used for knowledge discovery from databases. Cluster is a group of objects that belongs to the same class. In other words, similar objects are grouped in one cluster and dissimilar objects are group in another cluster. Clustering methods can be classified into Partitioning Method, Hierarchical Method, Density-based Method. Clustering analysis is used in several applications like market research, pattern recognition, data analysis. K-means clustering is well known partitioning method. But this method has problem of empty cluster. The problems with existing system[6] were analysis, capture, search, sharing, storage, transfer, visualization, querying-updating. These problems can be reduced by using proposed algorithm. In this paper clustering and proposed algorithm is discussed.

References

• Piatetsky-Shapiro, Gregory (1991), Discovery, analysis, and presentation of strong rules, in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, MA.
• Agrawal, R.; Imieliński, T.; Swami, A. (1993). "Mining association rules between sets of items in large databases". Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93. p. 207. doi:10.1145/170035.170072. ISBN 0897915925.
• Hahsler, Michael (2005). "Introduction to arules – A computational environment for mining association rules and frequent item sets" (PDF). Journal of Statistical Software.
• Michael Hahsler (2015). A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules. http://michael.hahsler.net/research/association_rules/measures.html
• Hipp, J.; Güntzer, U.; Nakhaeizadeh, G. (2000). "Algorithms for association rule mining --- a general survey and comparison". ACM SIGKDD Explorations Newsletter 2: 58. doi:10.1145/360402.360421.
• Tan, Pang-Ning; Michael, Steinbach; Kumar, Vipin (2005). "Chapter 6. Association Analysis: Basic Concepts and Algorithms" (PDF). Introduction to Data Mining. Addison-Wesley. ISBN 0-321-32136-7.
• Pei, Jian; Han, Jiawei; and Lakshmanan, Laks V. S.; Mining frequent itemsets with convertible constraints, in Proceedings of the 17th International Conference on Data Engineering, April 2–6, 2001, Heidelberg, Germany, 2001, pages 433-442
• Agrawal, Rakesh; and Srikant, Ramakrishnan; Fast algorithms for mining association rules in large databases, in Bocca, Jorge B.; Jarke, Matthias; and Zaniolo, Carlo; editors, Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile, September 1994, pages 487-499
• Zaki, M. J. (2000). "Scalable algorithms for association mining". IEEE Transactions on Knowledge and Data Engineering 12 (3): 372–390. doi:10.1109/69.846291.
• Hájek, Petr; Havel, Ivan; Chytil, Metoděj; The GUHA method of automatic hypotheses determination, Computing 1 (1966) 293-308
• Hájek, Petr; Feglar, Tomas; Rauch, Jan; and Coufal, David; The GUHA method, data preprocessing and mining, Database Support for Data Mining Applications, Springer, 2004, ISBN 978-3-540-22479-2
• Omiecinski, Edward R.; Alternative interest measures for mining associations in databases, IEEE Transactions on Knowledge and Data Engineering, 15(1):57-69, Jan/Feb 2003
• Aggarwal, Charu C.; and Yu, Philip S.; A new framework for itemset generation, in PODS 98, Symposium on Principles of Database Systems, Seattle, WA, USA, 1998, pages 18-24
• Brin, Sergey; Motwani, Rajeev; Ullman, Jeffrey D.; and Tsur, Shalom; Dynamic itemset counting and implication rules for market basket data, in SIGMOD 1997, Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 1997), Tucson, Arizona, USA, May 1997, pp. 255-264
• Piatetsky-Shapiro, Gregory; Discovery, analysis, and presentation of strong rules, Knowledge Discovery in Databases, 1991, pp. 229-248