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Semi-supervised clustering is a popular machine learning technique, used for challenge data categorization tasks, when some prior knowledge is available to users. In this paper, we report the empirical studies on our newly proposed semi-supervised clustering framework, which utilizes multiple viewpoints for the similarity measure, with the help of the prior knowledge. Two different MVS-based approaches...
Schema matching is widely used in many database applications, such as, data integration, data warehouse, data spaces, and ontology merging. In this paper, we propose multi-schema matching based on web structured information sources. There are two meanings at this point. Traditional matching techniques mainly address matching tasks between two attributes, namely pair wise-attribute correspondence....
Schema matching plays an important role in many database applications, such as ontology merging, data integration, data warehouse and dataspaces. The problem of schema matching is to find the semantic correspondence between attributes of schemas to be matched. In this paper, we propose multi-schema matching based on clustering techniques. Traditional matching techniques mainly address matching tasks...
Accidental releases and improper disposal of hazardous chemicals has led to widespread chemical contamination of subsurface soils and water-bearing formations. Effective remediation and restoration of such contaminated sites is dependent upon knowledge of the contaminant's mass and distribution within the aquifer. Recent research has shown that the estimation of certain metrics which summarize the...
An important task in Music Information Retrieval is content-based similarity retrieval in which given a query music track, a set of tracks that are similar in terms of musical content are retrieved. A variety of audio features that attempt to model different aspects of the music have been proposed. In most cases the resulting audio feature vector used to represent each music track is high dimensional...
Locality sensitive hashing (LSH) is quite popular in high dimensional data indexing. However, most of existing methods perform hashing in an unsupervised way, that is to say, hash functions are randomly generated without the prior information of the data. In this paper, we propose two improved LSH algorithms based on weakly supervised learning technique, which need only small quantities of labeled...
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