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Clustering has been used as a tool for understanding the content of large text document sets. As the volume of stored data has increased, so has the need for tools to understand output from clustering algorithms. We developed a new visual interface to meet this demand. Our interface helps non-technical users understand documents and clusters in massive databases (e.g., document content, cluster sizes, distances between clusters, similarities of documents within clusters, extent of cluster overlaps) and evaluate the quality of output from different clustering algorithms. When a user inputs a keyword query describing his/her interests, our system retrieves and displays documents and clusters in three dimensions. More specifically, given a set of documents modeled as vectors in an orthogonal coordinate system and a query, our system finds three orthogonal coordinate axes that are most relevant to generate a display (or users may choose any three orthogonal axes). We conducted implementation studies to demonstrate the value of our system with an artificial data set and a de facto benchmark news article dataset from the United States NIST Text REtrieval Competitions (TREC).

This paper describes a novel approach of detecting misalignment at the time of aligning two different ontologies, and of eliminating the misalignments Our objective is to reduce limitation of a specific technique of ontology alignment Two aligned sets extracted by different alignment techniques from the same pair of ontology, are fed to the misalignment detection and elimination process to produce better alignments Our experiments demonstrate that our method, taking advantage of misalignment detection and elimination, shows a good recall and precision.

We will present a new algorithm for improving the retrieval performance using query expansion, based on a hierarchy of clusters. In order to create this hierarchical data structure, a clustering algorithm is executed multiple times with different initial conditions. With the aid of this hierarchical data structure, we have achieved significant improvement in retrieval performance over previously known methods in terms of both recall and precision. In our experiments with Japanese patent data, we have employed a co-clustering algorithm as a clustering method.

The recent popularity of digital media such as CD-ROM and the Internet has prompted exploration of techniques for embedding data, either visibly or invisibly, into text, image, and audio objects. Applications of such data embedding include copyright identification, theft deterrence, and inventory.

The present paper emphasized a total approach to 3-D computer animation, based on AVENUE. An integrated animation system needs to support all kinds of animation, and AVENUE has this potential, since it supports smooth object modeling, motion specification, and rendering. The authors describe the system concept[1], a modeling method using a geometry language, and a motion specification method using a 3-D interactive parametric keyframe and rule based motion analysis. They also explain a rendering technique that uses sub-division technique for CSG(Constructive Solid Geometry) with deformation.