Fuzzy Clustering by Fast Search and Find of Density Peaks

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data-point as compared to its surrounding neighbors and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. Fuzzy-CFSFDP uses the fuzzy rules based on aforementioned assumption for the selection of cluster centers, adaptively. We performed a number of experiments on eight synthetic clustering datasets and compared the resulting clusters with the state of the art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.

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

[2]  Sang-Yeob Oh,et al.  Robust vocabulary recognition clustering model using an average estimator least mean square filter in noisy environments , 2013, Personal and Ubiquitous Computing.

[3]  Kun Li,et al.  Personalized multi-modality image management and search for mobile devices , 2013, Personal and Ubiquitous Computing.

[4]  Matthew Karl Ellis Shaw,et al.  K-means clustering with automatic determination of K using a Multiobjective Genetic Algorithm with applications to microarray gene expression data , 2015 .

[5]  Peter Rossmanith,et al.  Exact algorithms for problems related to the densest k-set problem , 2014, Inf. Process. Lett..

[6]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[7]  Cor J. Veenman,et al.  A Maximum Variance Cluster Algorithm , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Pasi Fränti,et al.  Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Dit-Yan Yeung,et al.  Robust path-based spectral clustering , 2008, Pattern Recognit..

[11]  Aristides Gionis,et al.  Clustering aggregation , 2005, 21st International Conference on Data Engineering (ICDE'05).

[12]  Katharina Gaus,et al.  Analysis of Nanoscale Protein Clustering with Quantitative Localization Microscopy , 2015 .