Data quality: management and technology
Data quality: management and technology
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th 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
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Shrinking-Based Clustering Approach for Multidimensional Data
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we present continuous research on data analysis based on our previous work on the shrinking approach. Shrinking[19] is a novel data preprocessing technique which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitation[16]in the real world. It can be applied in many data mining fields. In this approach data are moved along the direction of the density gradient, thus making the inner structure of data more prominent. It is conducted on a sequence of grids with different cell sizes. In this paper, we applied the Fuzzy concept to improve the performance of the shrinking approach, targeting the better decision making for the movement for individual data points in each iteration. This approach can assist to improve the performance of existing data analysis approaches.