Computer-assisted reasoning in cluster analysis
Computer-assisted reasoning in cluster analysis
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bayesian classification (AutoClass): theory and results
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)
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Using Self-Similarity to Cluster Large Data Sets
Data Mining and Knowledge Discovery
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
Tracking Clusters in Evolving Data Sets
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
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
The practical method of fractal dimensionality reduction based on z-ordering technique
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Moreover, there will exist more or less similarities among these large amounts of initial cluster results in a real-life data set. Accordingly, an analyser will have difficulty implementing further analysis if they know nothing about these similarities. Therefore, it is very valuable to analyse these similarities and construct the hierarchy structures of the initial clusters. The traditional cluster methods are unfit for this cluster postprocessing problem for their favour of finding the spherical shape clusters, impractical hypothesis and multiple scans of the data set. Based on multifractal theory, we propose the MultiFractal-based Cluster Hierarchy Optimisation (MFCHO) algorithm, which integrates the cluster similarity with cluster shape and cluster distribution to construct the cluster hierarchy tree from the disjoint initial clusters. The elementary time-space complexity of the MFCHO algorithm is presented. Several comparative experiments using synthetic and real-life data sets show the performance and the effectivity of MFCHO.