Rank of Hangzhou Public Free-Bicycle System rent stations by improved k-means clustering

In China, Hangzhou is the first city to set up the Public Free-Bicycle System. There are many and many technology problems in the decision of intelligent dispatch. In this paper, we investigate the rank of Hangzhou Public Free-Bicycle System rent station with improved k-means clustering. Actually, ranking rent station is a very challenge work. In this paper, an improved k-means clustering algorithm is proposed for efficient getting the rank of Hangzhou Public Free-Bicycle System rent s-tations. At first, by passing over the cruel one week's database, a rent-return database is initialed. Then, the rank is determined from the borrow-return database.

[1]  Ujjwal Maulik,et al.  An evolutionary technique based on K-Means algorithm for optimal clustering in RN , 2002, Inf. Sci..

[2]  Boris Mirkin.  Clustering Algorithms: a Review , 1996 .

[3]  David J. Hand,et al.  Short communication: Optimising k-means clustering results with standard software packages , 2005 .

[4]  Boris Mirkin,et al.  Mathematical Classification and Clustering , 1996 .

[5]  M. Narasimha Murty,et al.  Efficient clustering of large data sets , 2001, Pattern Recognition.

[6]  Ahmed F. Ghoniem,et al.  K-means clustering for optimal partitioning and dynamic load balancing of parallel hierarchical N-body simulations , 2005 .

[7]  Georg Peters,et al.  Some refinements of rough k-means clustering , 2006, Pattern Recognit..

[8]  Paul S. Bradley,et al.  Clustering via Concave Minimization , 1996, NIPS.

[9]  R. J. Kuo,et al.  Application of ant K-means on clustering analysis , 2005 .

[10]  Shehroz S. Khan,et al.  Cluster center initialization algorithm for K-means clustering , 2004, Pattern Recognit. Lett..

[11]  Shokri Z. Selim,et al.  K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Boris G. Mirkin,et al.  Concept Learning and Feature Selection Based on Square-Error Clustering , 1999, Machine Learning.

[13]  Bjarni Bödvarsson,et al.  Extraction of time activity curves from positron emission tomography: K-means clustering or non-negative matrix factorization , 2006, NeuroImage.

[14]  David G. Stork,et al.  Pattern Classification , 1973 .

[15]  Makoto Otsubo,et al.  Computerized identification of stress tensors determined from heterogeneous fault-slip data by combining the multiple inverse method and k-means clustering , 2006 .