In this paper we analyze the performance of clustering methods on the task of constructing community models for the users of large Web sites.

In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use association rules having high confidence to construct a hierarchical sequence of clusters. A specific metric is introduced for measuring the quality of the resulting clusterings. Practical consequences are discussed in view of some experiments on real life datasets.

Abstract. In this paper we propose a methodology for organising the users of an information providing system into groups with common interests (communities). The communities are built using unsupervised learning techniques on data collected from the users (user models). We examine a system that filters news on the Internet, according to the interests of the registered users. Each user model contains the user’s interests on the news categories covered by the information providing system. Two learning algorithms are evaluated: COBWEB and ITERATE. Our main concern is whether meaningful communities can be constructed. We specify a metric to decide which news categories are representative for each community. The construction of meaningful communities can be used for improving the structure of the information providing system as well as for suggesting extensions to individual user models. Encouraging results on a large data-set lead us to consider this work as a first step towards a method that can easily be integrated in a variety of information systems. 1

Until recently, electronic markets were dominated by the combination of static offer plus fixed pricing policies. Static offer schemes assume that all users have the same criteria which may not meet the requirements of all potential buyers. A fixed price might not always reflect the current market balance of supply and demand and the specific valuation of a single buyer. In this paper we propose an agent-mediated insurance brokering system using a flexible negotiation model that includes multi-attribute bidding as well as some kind of learning capabilities.

Current directory-based hierarchical file systems have many limitations as the amount of unstructured data possessed by individual user is increasing continuously. One of the most significant problems is that users usually have difficulties searching, navigating, and organizing their files since useful semantic information describing a file is not used in the current directory-based system. To solve this problem, several research groups have suggested attribute-based file naming systems. However, their approaches have not been widely used because of lack of semantic information. In this paper, we describe the ontology-based semantic file naming approach that employs the hierarchical conceptual clustering technique to capture more complex semantic information from the set of file attributes. Ontologies, which play a major role on the Semantic Web, describe the semantics of data by organizing data into taxonomies of concepts and describing the relationships between concepts. To generate the ontology from the set of attribute-value pairs for files, we first extend one of the standard incremental hierarchical clustering techniques, COBWEB, and suggest the new clustering evaluation measure to guide search through the space of clustering. From the clustering result, we then generate the ontology and represent it by the RDF Schema. Our experimental results show that our extended clustering approach can produce a good quality of the concept hierarchy, and is computationally efficient and well suited to building the ontology-based semantic file system. 1

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Evidently there is a tremendous proliferation in the amount of information found today on the largest shared information source, the World Wide Web (or simply the Web). The process of finding relevant information on the web can be overwhelming. Even with the presence of today’s search engines that index the web it is hard to wade through the large number of returned documents in a response to a user query. This fact has lead to the need to organize a large set of documents (due to a user query or simply a collection of documents) into categories through clustering. It is believed that grouping similar documents together into clusters will help the users find relevant information quicker, and will allow them to focus their search in the appropriate direction. The purpose of this review is an attempt to explore the clustering techniques in the data mining literature and to report on their appropriateness for clustering large sets of web documents. The review is by no means complete but covers the most representative approaches for clustering. 1. Background The motivation behind clustering any set of data is to find inherent structure in the

When a partitional structure is derived from a data set using a data mining algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. To overcome this problem, the first clustering process proceeds to construct an initial partition. The partition is expected to imply the possible range in the number of final clusters. We apply center sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm, REIT, that achieves both efficiency and reliability. A number of experiments have been performed to show that the algorithm helps minimize the order bias effects.

Authors and affiliations are hidden for review purpose Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach.

The task of data mining is mainly concerned with the extraction of knowledge from large sets of data. Clustering techniques are usually used to find regular structures in data. Conceptual clustering is one technique that forms concepts out of data incrementally by subdividing groups into subclasses iteratively; thus building a hierarchy of concepts. This paper presents the use of conceptual clustering in data mining a large set of documents to find meaningful groupings among them. An incremental conceptual clustering technique based on probabilistic guidance function is implemented and tested against the data set for cohesion of the resulting cluster structure.