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Clustering in high dimensional data is an important task. Subspace clustering has emerged as a possible solution to the challenges associated with high dimensional clustering. A subspace cluster is a subset of points together with a subset of attributes, such that some category of value of cluster points has great aggregation in these attributes. This paper proposes a subspace clustering algorithm...
Sequential pattern mining is an important and useful tool with broad applications, such as analyzing customer purchase behavior, recommending services to customers, and so on. It is challenging since explosive number of subsequences need to be examined and both the memory and computational cost are becoming extremely expensive when the sequence database grows huge. Many previous algorithms developed...
Reverse top- k queries are rank-aware problems from the view of the product manufacturers, and have gained popularity in recent studies. In this paper, we propose a novel online algorithm for processing monochromatic reverse top- k queries. The algorithm is based on dual plane transformation and is about 10 times faster than existing algorithms. We also propose a data structure RIL (Ranking Inverted...
In recent years, extensive researches have been conducted to develop approaches to answer two major challenges for collaborative filtering problems, namely sparsity and scalability. In this paper, we propose a novel collaborative filtering recommendation approach to alleviate these challenges. Our approach firstly converts the user-item ratings matrix to user-class matrix, and hence increases greatly...
Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based...
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