Clustering Method Based on Graph Data Model and Reliability Detection

doi:10.16182/j.issn1004731x.joss.201806013

Abstract

Abstract: For the data in feature space, traditional clustering algorithm can take clustering analysis directly. High-dimensional spatial data cannot achieve intuitive and effective graphical visualization of clustering results in 2D plane. Graph data can clearly reflect the similarity relationship between objects. According to the distance of the data objects, the feature space data are modeled as graph data by iteration. Cluster analysis based on modularity is carried out on the modeling graph data. The two-dimensional visualization of non-spherical-shape distribution data cluster and result is achieved. The concept of credibility of the clustering result is proposed, and a method is proposed, which the Page Rank algorithm is used to calculate the reliability of clustering results.

Key words: data mining, clustering, graph data modeling, modularity, Page Rank algorithm

CLC Number:

TP391.9

Cheng Yanyun, Bian Huisong, Bian Changsheng. Clustering Method Based on Graph Data Model and Reliability Detection[J]. Journal of System Simulation, 2018, 30(6): 2102-2109.

References

[1] Park H S, Jun C H.A simple and fast algorithm for K-medoids clustering[J]. Expert Systems with Applications (S0957-4174), 2009, 36(2): 3336-3341.
[2] Ng R T, Han J.CLARANS: A Method for Clustering Objects for Spatial Data Mining[J]. IEEE Transactions on Knowledge & Data Engineering (S1041-4347), 2002, 14(5): 1003-1016.
[3] Guha S, Rastogi R, Shim K.ROCK: A Robust Clustering Algorithm for Categorical Attributes[C]// International Conference on Data Engineering, 1999. Proceedings. 2002: 345-366.
[4] Karypis G, Han E H, Kumar V.Chameleon: hierarchical clustering using dynamic modeling[J]. Computer (S0018-9162), 1999, 32(8): 68-75.
[5] Ankerst M, Breunig M M, Kriegel H P, et al.OPTICS: ordering points to identify the clustering structure[C]// Proceedings ACM SIGMOD International Conference on Management of Data, 1999, 28(2): 49-60.
[6] Sheikholeslami G, Chatterjee S, Zhang A.WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases[C]// Proceedings of the 24rd International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 1998: 428-439.
[7] Fisher D H.Knowledge acquisition via incremental conceptual clustering[J]. Machine Learning (S0885-6125), 1987, 2(2): 139-172.
[8] Leski J.Towards a robust fuzzy clustering[J]. Fuzzy Sets & Systems (S0165-0114), 2003, 137(2): 215-233.
[9] Carvalho F D A T D. Fuzzy c -means clustering methods for symbolic interval data[J]. Pattern Recognition Letters (S0167-8655), 2007, 28(4): 423-437.
[10] 赵京胜, 孙梦丹, 张丽. 一种有效的K-means初始中心优化算法[J]. 信息技术与信息化, 2016(5): 77-79.
Zhao J S, Sun M D, Zhang L.An effective k-means algorithm with initial center optimization[J], Information Technology and Information. 2016(5): 77-79.
[11] 蔡宇浩, 梁永全, 樊建聪, 等. 加权局部方差优化初始簇中心的K-means算法[J]. 计算机科学与探索, 2016, 10(5): 732-741.
Cai Y H, Liang Y Q, Fan J C, et al.Optimizing Initial Cluster Centroidss by Weighted Local Variance in K-means Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(5): 732-741.
[12] Newman M E, Girvan M.Finding and evaluating community structure in networks[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics (S1539-3755), 2004, 69(2 Pt 2): 026113.
[13] Clauset A, Newman M E, Moore C.Finding community structure in very large networks[J]. Physical Review E (S2470-0045), 2010, 70(6 Pt 2): 264-277.
[14] Brin S, Page L.The anatomy of a large-scale hypertextual Web search engine[J]. Computer Networks and ISDN Systems (S0169-7552), 1998, 30: 107-117.
[15] 董骐瑞. k-均值聚类算法的改进与实现[D]. 吉林: 吉林大学软件学院, 2015: 43-46.
Dong Q R.Improvements and Implementation of k-means Clustering Algorithm[D]. Jilin: School of Software, Jilin University, 2015: 43-46.