Applied multivariate techniques
Applied multivariate techniques
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Volatility model based on multi-stock index for TAIEX forecasting
Expert Systems with Applications: An International Journal
Probability-based text clustering algorithm by alternately repeating two operations
Journal of Information Science
Two novel fuzzy clustering methods for solving data clustering problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
Clustering analysis is to identify inherent structures and discover useful information from large amount of data. However, the decision makers may suffer insufficient understanding the nature of the data and do not know how to set the optimal parameters for the clustering method. To overcome the drawback above, this paper proposes a new entropy clustering method using adaptive learning. The proposed method considers the data spreading to determine the adaptive threshold within parameters optimized by adaptive learning. Four datasets in UCI database are used as the experimental data to compare the accuracy of the proposed method with the listing clustering methods. The experimental results indicate that the proposed method is superior to the listing methods.