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• W2547398736 abstract "Recently data mining over the uncertain data grabed more attention of the data mining community. To classify or cluster the valid or certain data, there are different approaches like DTL, Rule based Classification, Naive Bayes Classification and many more techniques. Its easy to classify the certain data but classification of the uncertain data is bit difficult. The uncertainty occurs in the a data because of the impresize measurement of the results, like scientific results, data from sensor netowork, measuring temperature, humidity, pressure and so on. From such a sources there is possibility of getting the uncertainty in a data. Main task is to handle the uncertainty of the data in order to classify or cluster it. It comes under the NP-Hard problems. Solving this problem, different approaches are available. We study the problem of clustering and classification of uncertain objects whose locations are described by probability density functions (pdf) means valued uncertainty. We show that the averaging algorithm with KMean algorithm, which generalises the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that it does the averaging of the range of uncertain attribute. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. Previous literature has applied bounding-box-based techniques to reduce the number of Expected Distance(ED) calculation. We use pruning techniques that are based on Voronoi diagrams to further reduce the number of expected distance calculation. These techniques are analytically proven to be more effective than the basic boundingbox-based technique previously known in the literature. We use R-tree index to organise the uncertain objects in groups so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques, and extend the studies to different datasets. We show that these techniques are additive and, when used in combination, significantly outperform previously known methods." @default.
• W2547398736 created "2016-11-11" @default.
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• W2547398736 date "2012-01-01" @default.
• W2547398736 modified "2023-09-24" @default.
• W2547398736 title "Uncertain Numerical Data Clustering Using VORONOI Diagram and R-Tree With Ensemble SVM" @default.
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