Paper Info
Title
A mixed attributes oriented dynamic SOM fuzzy cluster algorithm for mobile user classification.
Abstract
In the field of mobile user behavior analysis, clustering algorithm is used to do the user classification. Usually the mobile user’s dataset is mixed, which contains both numerical and categorical type of data. It leads to inaccurate results when doing the user classification using traditional algorithms like K-Means and which are affected by the initialization process extremely. On the other hand, K-Prototypes algorithm is used to process the mixed data. It is difficult to ascertain the coefficient of classification attribute weight. Based on the above problems, this paper proposes a mixed attributes oriented dynamic SOM fuzzy cluster algorithm (D-SOMFCM-OMA) for mobile user classification. Firstly, the algorithm proposed in this paper gives the primary clustering using Self-Organizing feature Map (SOM) to get the initial clustering parameters. As the preprocessing of clustering, this step reduces effects caused by inappropriate initialization. Then, the algorithm utilizes the improved dynamic fuzzy K-Prototypes cluster method in the second-time clustering to classify users dynamically. It calculates weight of all kinds of attributes according to the proportion of the attribute and uses fine-tuned coefficient to adjust the attribute weight. For improving the clustering effect, the algorithm uses Jaccard distance to calculate the distance in the mixed attribute variables. Further, this paper defines the user mean membership threshold which is an indicator to determine whether different groups need to be added. Finally, some comparison experiments are conducted on the UCI standard datasets to show the advantage of the improved fuzzy clustering algorithm oriented the mixed attributes (IFCM-OMA). Also the other experiment using dataset of UCI verify the validity of the algorithm of D-SOMFCM-OMA.
Year
DOI
Venue
2020
10.1016/j.ins.2019.12.019
Information Sciences
Keywords
Field
DocType
Mobile user classification,Fuzzy cluster,Self-Organizing feature map
Primary clustering,Fuzzy clustering,Categorical variable,Fuzzy logic,Algorithm,Preprocessor,Jaccard index,Initialization,Cluster analysis,Mathematics
Journal
Volume
ISSN
Citations 
515
0020-0255
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Guangxia Xu1429.46
Linghao Zhang200.34
Chuang Ma3167.00
Yanbing Liu415516.38