Takahisa Ando

Discovery of emerging patterns from nearest neighbors

In this paper, we propose a scalable classifier that uses jumping emerging patterns (JEPs), which are combinations of values that occur in one class. The original classifier, DeEPs, is an instance-based classifier that operates on all instances in real-time. It discovers maximal patterns that occur throughout the entire database and identifies JEPs by using these patterns. The necessary computational effort, though, is likely to increase when DeEPs is applied to a large database. Our proposed classifier operates on the nearest neighbors of a test instance. This reduction of instances improves scalability as the database volume increases. Moreover, our classifier imposes a restriction regarding JEPs discovery, so that it excludes patterns that cannot be identified as either correct JEPs or JEPs caused by the maximal patterns missing from nearest neighbors. These probably incorrect JEPs are specialized with additional items and participate in class determination. Our classifier perform significantly faster with these two enhancements, while it remains as accurate as the original classifier.

Facilitating Search through Visualized Results

This paper presents a search engine interface enriching search results with thumbnails of documents such as reports, presentation materials, spreadsheets, and so on. Usual search results consist of text information, such as the title and its snippet of each document. Our interface adds the thumbnails of documents listed in search results for assisting the search processes of users. We evaluate the interface on a web site search engine. The experimental results show that our interface enables users to find their target documents more efficiently than using an interface without thumbnails.