Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood

However, in general, the parameters of density-based clustering algorithms are usually difficult to select. So, in order to make the density-based clustering algorithms more robust, the extension with fuzzy set theory has attracted a lot of attentions recently. The fuzzy neighborhood DBSCAN (FNDBSCAN) is a typical one with this idea. But FN-DBSCAN usually requires a time complexity of O(n2) where n is the number of data in the data set. This implies that the algorithm is not suitable for the work with large scale data sets.

[1]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[2]  Fionn Murtagh,et al.  Algorithms for hierarchical clustering: an overview , 2012, WIREs Data Mining Knowl. Discov..

[3]  Ming-Syan Chen,et al.  Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging , 2005, IEEE Trans. Knowl. Data Eng..

[4]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[5]  Chris H. Q. Ding,et al.  K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization , 2004, SAC '04.

[6]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[7]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[8]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[9]  Hans-Peter Kriegel,et al.  Density‐based clustering , 2011, WIREs Data Mining Knowl. Discov..

[10]  Yuqing Song,et al.  A unique property of single-link distance and its application in data clustering , 2011, Data Knowl. Eng..

[11]  Ke-Lin Du,et al.  Clustering: A neural network approach , 2010, Neural Networks.

[12]  E. N. Nasibov.  A robust algorithm for solution of the fuzzy clustering problem on the basis of the fuzzy joint points method , 2008 .

[13]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[14]  Michalis Vazirgiannis,et al.  Cluster validity methods: part I , 2002, SGMD.

[15]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[16]  Efendi N. Nasibov,et al.  A new unsupervised approach for fuzzy clustering , 2007, Fuzzy Sets Syst..

[17]  Chu-Sing Yang,et al.  A time-efficient pattern reduction algorithm for k-means clustering , 2011, Inf. Sci..

[18]  Efendi N. Nasibov,et al.  Robustness of density-based clustering methods with various neighborhood relations , 2009, Fuzzy Sets Syst..

[19]  Alexander Hinneburg,et al.  DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation , 2007, IDA.

[20]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[22]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[23]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

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

[25]  D. Bhattacharyya,et al.  A Clustering Technique using Density Difference , 2007, 2007 International Conference on Signal Processing, Communications and Networking.

[26]  Zhexue Huang,et al.  CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND CATEGORICAL VALUES , 1997 .

[27]  Adrian E. Raftery,et al.  Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST , 2003, J. Classif..