Optimum threshold of group formation in multiagents

This paper presents a simple multi-agent model of group formation, while the behaviour is complicated. The statistical characteristics of the formation are suddenly changed at some states. Their agents are carrying characteristic vectors, meet each other in nondimensional free spaces without any restrictions randomly, and the agents make groups in the spaces if their characteristics are similar, that is, they have high similarities. Actually, for a given threshold on similarities, when the characteristic vectors between two agents are similar in the threshold, the agents join into one. They are not only for agents but also for groups, i.e. two groups can become into one if they are similar, and we repeat it. On the other hand, making groups decrease a satisfaction on groups. In this paper, we show, for a given threshold, there is not only an optimal threshold to maximize the satisfactions among groups which satisfy the threshold, but also the other one to minimize the satisfactions. The thresholds to maximize satisfactions are experimentally approximated by some function on the size of characteristic vectors rather than the number of agents

Hierarchical Summarizing and Evaluating for Web Pages

Abstract. In this investigation we propose a novel summarization method of Web pages using hierarchical expression. We discuss close relationship between summarization and hierarchical clustering to obtain the results, and we examine how to evaluate hierarchical summarization based on both correlation and structural aspects. We describe some experimental results using NTCIR Web documents to examine our method.

Clustering Stream Data by Regression Analysis

In data clustering, many approaches have been proposed such as K-means method and hierarchical method. One of the problems is that the results depend heavily on initial values and criterion to combine clusters. In this investigation, we propose a new method to cluster stream data while avoiding this deficiency. Here we assume there exists aspects of local regression in data. Then we develop our theory to combine clusters using values by regression analysis as criterion and to adapt to stream data. We examine experiments and show how well the theory works