Introduction to algorithms
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
Regular Article: Randomized Nonlinear Projections Uncover High-Dimensional Structure
Advances in Applied Mathematics
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
Optimal multi-step k-nearest neighbor search
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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
A Shrinking-Based Clustering Approach for Multidimensional Data
IEEE Transactions on Knowledge and Data Engineering
Data gravitation based classification
Information Sciences: an International Journal
Automatic parameter determination in subspace clustering with gravitation function
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
A DGC-based data classification method used for abnormal network intrusion detection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Adapting OLAP analysis to the user's interest through virtual cubes
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Gravitation based classification
Information Sciences: an International Journal
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Existing data analysis techniques have difficulty in handling multi-dimensional data. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitation[22] in the real world. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multi-dimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus generating condensed, widely-separated clusters. Following data shrinking, clusters are detected by finding the connected components of dense cells. The data-shrinking and cluster-detection steps are conducted on a sequence of grids with different cell sizes. The clusters detected at these scales are compared by a cluster-wise evaluation measurement, and the best clusters are selected as the final result. The experimental results show that this approach can effectively and efficiently detect clusters in both low- and high-dimensional spaces.