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This paper presents a novel approach to feature construction for structured data in order to enhance graph prediction classification performance. To this end we combine graph mining techniques with graph kernel based classifiers. The main idea is to employ efficient mining techniques to extract a set of patterns correlated with the target concept and use these, or a selected subset of these, to annotate...
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...
We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these kernels for support vector machines. Issues related to the nature of geographic data such as autocorrelation and directionality are investigated.
Recently, many commercial products, such as Google Trends and Yahoo! Buzz, are released to monitor the past search engine query frequency trend. However, little research has been devoted for predicting the upcoming query trend, which is of great importance in providing guidelines for future business planning. In this paper, a unified solution is presented for such a purpose. Besides the classical...
We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a pre-processing step without sacrificing efficiency. The key is a partial evaluation...
For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features...
This paper proposes a support system for composing good titles for research papers in order to reach new audiences. Our system takes titles as input. The system evaluates title understandability and interest level of a title. The system ranks titles and outputs a title list. Users are able to recompose their titles by referring to the list and each evaluation value. Using the system, users can obtain...
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