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Full-Text Articles in Computer Sciences
Towards Solving Similarity Search Problems Using Fuzzy Concept For Multi-Dimensional Data, Yong Shi
Towards Solving Similarity Search Problems Using Fuzzy Concept For Multi-Dimensional Data, Yong Shi
Faculty and Research Publications
In this paper, we present continuous research on data analysis based on our previous work on similarity search problems. PanKNN[13] is a novel technique which explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively select data points which are closest to Q. It can be applied in various data mining fields. In this paper, we applied the Fuzzy concept to improve the performance of PanKNN, targeting the better decision making for the calculation of the distance between a data …
Subcoid: An Attempt To Explore Cluster-Outlier Iterative Detection Approach To Multi-Dimensional Data Analysis In Subspace, Yong Shi
Faculty and Research Publications
Many data mining algorithms focus on clustering methods. There are also a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Clusters and outliers should be treated as the concepts of the same importance in data analysis. In our previous work [22] we proposed a cluster-outlier iterative detection algorithm in full data space. However, in high dimensional spaces, for a given cluster or outlier, not all dimensions may be relevant to it. In this paper we extend …