Abstract
Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks. Unlike approaches using the Nystrom method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. Firstly, we propose a two-stage preference clustering framework. In this framework, we make use of random Fourier features to map the preference matrix into the feature matrix, soon afterwards, utilize the traditional k-means approach to cluster preference data in the transformed feature space. Compared with traditional preference clustering, our method solves the problem of insufficient memory and greatly improves the efficiency of the operation. Experiments on movie data sets containing 100 000 ratings, show that the proposed method is more effective in clustering accuracy than the Nystrom and k-means, while also achieving better performance than these clustering approaches.
Highlights
With the rapid development of information technology, data storage has been made relatively inexpensive and abundant, resulting in extremely large data sets
Compared with the traditional preference clustering approach, this paper makes the following contributions: (1) We present a two-stage framework for clustering preference data
We can see that for the same data set, our PCRFF approach is superior to the k-means and Nystrom approaches
Summary
With the rapid development of information technology, data storage has been made relatively inexpensive and abundant, resulting in extremely large data sets. Data mining provides us with an effective way to explore and analyze hidden patterns behind these data. These data sets share one prominent feature: which is enormity in size with tens of thousands of objects and features. Data sets are often sparse, so, how to excavate hidden patterns is a important problem. Clustering is an effective method which groups a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups[1].
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