Hybrid framework for DBSCAN algorithm using fuzzy logic

Data mining process is to obtain information from a data set and then convert it into an understandable and meaningful information for further use. DBSCAN, a density based clustering algorithm, identifies clusters of varying shape and outliers. DBSCAN is based on bivalent logic. Therefore it can only detect objects as completely belonging to a particular cluster or not wholly belonging to it. In this paper, a framework of methodology of DBSCAN algorithm with the integration of fuzzy logic is proposed. The extent to which an object belongs to a particular cluster will be determined using membership values. The improved version of DBSCAN algorithm will be the hybridization of DBSCAN algorithm with fuzzy if-then rules.

[1]  Michal Daszykowski,et al.  Revised DBSCAN algorithm to cluster data with dense adjacent clusters , 2013 .

[2]  Asieh Ghanbarpour,et al.  EXDBSCAN: An extension of DBSCAN to detect clusters in multi-density datasets , 2014, 2014 Iranian Conference on Intelligent Systems (ICIS).

[3]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4]  Shraddha K. Popat.  Review and Comparative Study of Clustering Techniques , 2014 .

[5]  Peter Kulchyski.  and , 2015 .

[6]  Deyi Zhou,et al.  A new clustering algorithm based on distance and density , 2005, Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005..

[7]  Simon Fong,et al.  DBSCAN: Past, present and future , 2014, The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014).

[8]  Glory H. Shah.  An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional data sets , 2012, 2012 Nirma University International Conference on Engineering (NUiCONE).

[9]  Anand Singh Jalal,et al.  A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases , 2010 .

[10]  P. Viswanath,et al.  Rough-DBSCAN: A fast hybrid density based clustering method for large data sets , 2009, Pattern Recognit. Lett..

[11]  Cheng-Fa Tsai,et al.  A new data clustering approach for data mining in large databases , 2002, Proceedings International Symposium on Parallel Architectures, Algorithms and Networks. I-SPAN'02.

[12]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Peng Liu,et al.  VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise , 2007, 2007 International Conference on Service Systems and Service Management.

[14]  M. Parimala,et al.  A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases , 2011 .

[15]  Shikha Agrawal,et al.  Modification of Density Based Spatial Clustering Algorithm for Large Database Using Naive's Bayes' Theorem , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.