Introduction to algorithms
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
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)
Data mining: concepts and techniques
Data mining: concepts and techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 approach for multi-dimensional data analysis
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A multi-prototype clustering algorithm
Pattern Recognition
Proceedings of the 46th Annual Southeast Regional Conference on XX
Approximate minimum spanning tree clustering in high-dimensional space
Intelligent Data Analysis
Grid-based clustering algorithm based on intersecting partition and density estimation
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Towards improving a similarity search approach
Proceedings of the 48th Annual Southeast Regional Conference
An approach to reshaping clusters for nearest neighbor search
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Learning a subspace for clustering via pattern shrinking
Information Processing and Management: an International Journal
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Existing data analysis techniques have difficulty in handling multidimensional data. Multidimensional data has been a challenge for data analysis because of the inherent sparsity of the points. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of distribution of data. 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 multidimensional 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 (and evaluated by their compactness). 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.