SCuBA: Subspace Clustering Based Analysis

Arizona State University, Computer Science and Engineering, Data Mining and Machine Learning

Overview

Researchers spend considerable time searching for relevant papers on the topic in which they are currently interested. Often, despite having similar interests, researchers in the same lab do not find it convenient to share results of bibliographic searches and thus conduct independent time-consuming searches. Research paper recommender systems can help the researcher avoid such time-consuming searches by allowing each researcher to automatically take advantage of previous searches performed by others in the lab. Existing recommender systems were developed for commercial domains to assist users by focusing towards products of their interests. Unlike those domains, the research paper domain has relatively few users when compared with the huge number of research papers. Here we present a novel system to recommend relevant research papers to a user based on the user's recent querying and browsing habits. The core of the system is a scalable subspace clustering algorithm (SCuBA) that performs well on the sparse, high-dimensional data collected in this domain.

About SCuBA Software

This software package is prepared in Java. It is provided free of charge to the research community as an academic software package with no commitment in terms of support or maintenance. Please read the README.TXT file in the package for further help in executing the software. Please refer to the paper below for data preperation and experimentation.

Downloads

• Java package can be downloaded here.

README provides the details about how to run, how to prepare the data, and how to read the results.

People

• ASU Data Mining and Machine Learning Lab: Nitin Agarwal, Huan Liu, Ehtesham Haque, Lance Parsons

References

• Nitin Agarwal, Ehtesham Haque, Huan Liu, and Lance Parsons. ``A Subspace Clustering Framework for Research Group Collaboration". International Journal of Information Technology and Web Engineering, 1(1), pdf, pp 35 - 58, January - March, 2006

• Nitin Agarwal, Ehtesham Haque, Huan Liu, Lance Parsons. "Research Paper Recommender System: A Subspace Clustering Approach", The 6th International Conference on Web-Age Information Management (WAIM 2005), pp. 475 - 491, October 11-13, 2005, Hang Zhou, China. pdf.
• Lance Parson, Ehtesham Haque, and Huan Liu. "Subspace Clustering for High Dimensional Data - A Review", SIGKDD Explorations,  pdf, 6(1): 90 - 105, June, 2004.
• Lance Parson, Ehtesham Haque, and Huan Liu. "Evaluating Subspace Clustering Algorithms", Workshop on Clustering High Dimensional Data and Its Applications, SIAM International Conference on Data Mining 2004, April 24, 2004. Lake Buena Vista, Florida. pdf

Created on 02/04/2007