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Structured data is becoming increasingly abundant in many application domains recently. In this paper, as one of the correlation mining, we propose new data mining problems of finding frequent and correlated pairs of patterns in structured databases. First, we consider the problem of finding all frequent and correlated pattern pairs in two dimensional structured databases. Then, two kinds of top-k...
In some applications, the whole structure of the target data can be represented naturally in "multi-structured graphs" that are complex graphs whose vertices consist of aset of structured data such as itemsets, sequences and so on. To catch the strong affinity relationship in multi-structured graphs, in this paper, we propose an algorithm named HFMG to discover novel and meaningful frequent...
In sequential pattern mining, languages based on regular expressions (RE) were proposed to restrict frequent sequences to the ones that satisfy user-specified constraints. In these languages, REs are applied over items. We propose a much powerful language, based on regular expressions, denoted RE-SPaM, where the basic elements are constraints over the attributes of the items. Expressions in this language...
Interpreted languages frequently suffer from higher processing times as compared to compiled approaches. Typically this happens when complex computations are performed. Array DBMSs, which extend database functionality with multidimensional array modeling and query support, find themselves in exactly this situation: queries often involve a large number of operations, and each such operation is applied...
Matrix factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-positive MF approach that approximates the features by using positive values for either users or items. We describe a momentum-based MF approach. A transductive...
An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent rArr consequent" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a...
Active KDD research groups typically make their software tools at disposal of others through the net. However, integration and reuse of these tools typically require a considerable amount of time to understand software scope and use, install it, transform data in a format compatible with the required input. This paper introduces a semantic based, service-oriented framework for tools sharing and reuse,...
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