Title :
Applications of clustering algorithms and self organizing maps as data mining and business intelligence tools on real world data sets
Author :
Singh, Lavneet ; Singh, Savleen ; Dubey, Parminder Kumar
Author_Institution :
Fac. of Manage. & Comput. Applic., R.B.S. Coll., Agra, India
Abstract :
Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation because of its efficiency in clustering large data sets. In this paper we present a comparative study on different clustering algorithms with respect to k - means clustering to work on large data sets. In this paper we present a comparison among some nonhierarchical and hierarchical clustering algorithms including SOM (Self-Organization Map) neural networks methods. Data were simulated considering correlated and uncorrelated variables, non overlapping and overlapping clusters with and without outliers. Tested with Telecommunication Users and Iris Flower data set, the comparative algorithms had demonstrated a very good classification performance. Experiments on a very large telecommunication data set set consisting of 1000 records and 32 categorical attributes & Iris Flower data set consisting of 150 samples show that the SOM clustering with respect to k means & hierarchical clustering algorithm is scalable in terms of both the number of clusters and the number of records.
Keywords :
competitive intelligence; data mining; pattern classification; pattern clustering; self-organising feature maps; Iris Flower data set; Telecommunication Users; business intelligence tool; clustering algorithms; data classification; data mining; k-means algorithm; k-means clustering; large data set clustering; neural network; nonhierarchical clustering algorithm; self-organizing map; Analysis of variance; Computer aided software engineering; Integrated circuits; Iris recognition; Variable speed drives; Data mining; Hierarchical clustering; K means clustering; Self organizing maps;
Conference_Titel :
Methods and Models in Computer Science (ICM2CS), 2010 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4244-9701-0
DOI :
10.1109/ICM2CS.2010.5706714