George Karypis

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Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available(More)
Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [4, 26]. From the early work it was clear that multilevel techniques held great promise; however,(More)
In this paper we present and study a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, find a k-way partitioning of the smaller graph, and then uncoarsen and refine it to construct a k-way partitioning for the original graph. These algorithms compute a k-way partitioning of a graph G = (V, E) in O(|E(More)
This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is(More)
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of <i>recommender systems</i>---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems(More)
Graph partitioning has extensive applications in many areas, including scientific computing, VLSI design, and task scheduling. The problem is to partition the vertices of a graph in p roughly equal parts, such that the number of edges connecting vertices in different parts is minimized. For example, the solution of a sparse system of linear equations Ax = b(More)
Over the years, frequent itemset discovery algorithms have been used to solve various interesting problems. As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in(More)
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We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called “recommender systems”. Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays,(More)