Algorithms for clustering data
Algorithms for clustering data
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Applications of Data Mining in Computer Security
Applications of Data Mining in Computer Security
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
An iterative initial-points refinement algorithm for categorical data clustering
Pattern Recognition Letters
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Finding Localized Associations in Market Basket Data
IEEE Transactions on Knowledge and Data Engineering
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On efficiently summarizing categorical databases
Knowledge and Information Systems
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
A new initialization method for categorical data clustering
Expert Systems with Applications: An International Journal
A new initialization method for clustering categorical data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 12.05 |
The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers. Currently, most methods of initialization cluster centers are mainly for numerical data. Due to lack of geometry for the categorical data, these methods used in cluster centers initialization for numerical data are not applicable to categorical data. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes algorithm. The method integrates the distance and the density together to select initial cluster centers and overcomes shortcomings of the existing initialization methods for categorical data. Experimental results illustrate the proposed initialization method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data objects.