Rough similarity degree and rough close degree in rough fuzzy sets and the applications

Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.

Static rough similarity degree and its applications

The definition of rough similarity degree is given based on the axiomatic similarity degree, and the properties of rough similarity degree are listed. Using the properties of rough similarity degree, the method of clustering in rough systems can be obtained. After clustering, a new sample can be recognized by the principle of maximal rough similarity degree.

Group Similarity and Social Influence Analysis in Online Communities

A fundamental open question in the analysis of social networks was to understand the evolution between similarity and group social ties.In general,two groups are similar for two distinct reasons:first,they grow to change their behaviors to the same group due to social influence;second,they tend to merge a group due to similar behaviors,where a process often is termed selection by sociologists.It was important to understand why two groups could merge and what led to high similarities for members in a group,influence or selection.In this paper,the techniques for identifying and modeling interactions between social influence and selection for different groups were developed.Different similarities were computed in three phases where groups came into being,before or after according to the number of common edits in Wikipedia.Experimental results showed selection played a more important role in two group merging.

Most similar maximal clique query on large graphs

This paper studies the most similar maximal clique query(MSMCQ).Given a graph G and a set of nodes Q,MSMCQ is to find the maximal clique of G having the largest similarity with Q.MSMCQ has many real applications including advertising industry,public security,task crowdsourcing and social network,etc.MSMCQ can be studied as a special case of the general set similarity query(SSQ).However,the MCs of G has several specialties from the general sets.Based on the specialties of MCs,we propose a novel index,namely MCIndex.MCIndex outperforms the state-of-the-art SSQ method significantly in terms of the number of candidates and the query time.Specifically,we first construct an inverted indexⅠfor all the MCs of G.Since the MCs in a posting list often have a lot of overlaps,MCIndex selects some pivots to cluster the MCs with a small radius.Given a query Q,we compute the distance from the pivots to Q.The clusters of the pivots assured not answer can be pruned by our distance based pruning rule.Since it is NP-hard to construct a minimum MCIndex,we propose to construct a minimal MCIndex onⅠ(v)with an approximation ratio 1+ln|Ⅰ(v)|.Since the MCs have properties that are inherent of graph structure,we further propose a S Index within each cluster of a MCIndex and a structure based pruning rule.S Index can significantly reduce the number of candidates.Since the sizes of intersections between Q and many MCs need to be computed during the query evaluation,we also propose a binary representation of MCs to improve the efficiency of the intersection size computation.Our extensive experiments confirm the effectiveness and efficiency of our proposed techniques on several real-world datasets.