Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
ACM Computing Surveys (CSUR)
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Potential-Based Hierarchical Clustering
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
An overview of clustering methods
Intelligent Data Analysis
Data gravitation based classification
Information Sciences: an International Journal
K-AP: Generating Specified K Clusters by Efficient Affinity Propagation
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic clustering based on universal gravitation model
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
PHA: A fast potential-based hierarchical agglomerative clustering method
Pattern Recognition
| Hi-index | 0.01 |
A novel clustering method called Clustering by Sorting Potential Values (CSPV) is proposed. The clustering is done in an efficient tree-growing fashion based on both the distances and the hypothetical potential values produced from the distribution of all the data points. The method is simple but is shown to be very effective in identifying different kinds of clusters. It outperforms four popular clustering methods in most of our experiments and is the only one that works for all the six studied data sets. Moreover, it is designed as a generic method which can be easily applied to different clustering problems.