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In this paper, an intelligent portfolio selection method based on Affinity Propagation clustering algorithm is proposed to solve the stable investment problem. The goal of this work is to minimize the volatility of the selected portfolio from the component stocks of S&P 500 index. Each independent stock can be viewed as a node in graph, and the similarity measurements of stock price variations...
Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE)...
Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective...
Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings...
Mining arbitrary shaped clusters in large data sets is an open challenge in data mining. Various approaches to this problem have been proposed with high time complexity. To save computational cost, some algorithms try to shrink a data set size to a smaller amount of representative data examples. However, their user-defined shrinking ratios may significantly affect the clustering performance. In this...
Finding similar crime case subsets is an important task for intelligence analysts in crime investigation. It can not only provide multiple clues to solve crimes but also improve efficiency to catch the criminals. However, the conventional approach by querying specific attributes in relational databases has two defects: first, it is relatively of poor efficiency when a lot of incidents have to be handled;...
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