Stock trend prediction based on fractal feature selection and support vector machine
Expert Systems with Applications: An International Journal
An IFS-based similarity measure to index electroencephalograms
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters) among which there exist more or less similarities and hi- erarchies. Accordingly, customer will have difficult to in- terpret and describe these large amounts of initial cluster results and hierarchies among them. Therefore, it is very valuable to analyze these similarities and construct hier- archy structures of the cluster results based on the sim- ilarities. The statistical cluster methods, the grid-based and density-based cluster methods and the model-based cluster algorithms are unfit for this post-processing cluster problem. Furthermore, this problem becomes more intri- cate in data stream environment for the constraint of single scan of stream data and the need of incremental cluster- ing. Based on multifractal theory, the Fractal-based Clus- ter Hierarchy Optimization (FCHO) algorithm is proposed, which integrate the cluster similarity with the cluster shape and the cluster distribution to construct cluster hierarchy tree from the disjoint initial clusters. The algorithm pro- posed is easy to realize, simple to understand and parame- ter self-adaptive. The elementary time-space complexity is presented and the experimental results using synthetic and real life data set show the performance and the effectivity of FCHO algorithm.