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Recently, every enterprise generates large volumes of high dimensional data on a regular basis. Complex data mining and analysis techniques are used to feasibly analyse this data. Feature selection aids in this by providing a reduced representation of this data while maintaining integrity. We propose a graph-based feature selection algorithm utilizing feature intercorrelation to construct a weighted...
We introduce a new trend detection problem inspired by real-time monitoring applications where the origin of the measurements is uncertain: The observed sequence under the alternative hypothesis is the result of a random switching between two sequences, each with a trend. The association between each measurement sample and the two sequences is unknown to the detector. We propose a Generalized Mann-Kendall...
Given a graph G = (V, E) with real-valued edge weights, the problem of correlation k-clustering with pre-clustered items is to extend a k-clustering of distinguished vertices of G (pre-clustered items) to partition all the vertices into clusters so as to minimize the total absolute weight of cut positive edges and uncut negative edges. This problem for general graphs is APX-complete. A polynomial...
Given a graph G=(V,E) with real positive edge weights, where each edge (u, v) is labeled either + or - depending on whether u and v have been deemed to be similar or dissimilar, the problem of correlation clustering with l clusters is to partition the vertices of G into at most l clusters to minimize the total weight of + edges between clusters and - edges within clusters. This problem for general...
The recent proliferation of graph data in a wide spectrum of applications has led to an increasing demand for advanced data analysis techniques. In view of this, many graph mining techniques, such as frequent subgraph mining and correlated subgraph mining, have been proposed. In many applications, both frequency and correlation play an important role. Thus, this paper studies a new problem of mining...
As a typical social media in Web 2.0 era, blogs have become more and more important to information diffusion. Different from the traditional news, the information spread on blogs is primarily driven by users and their relations. According to this phenomenon, this paper addresses the novel problem of measuring the influence of social structures on information diffusion. This paper extracts the hidden...
The correlation clustering problem has been introduced recently as a model for clustering data when a binary relationship between data points is known. Correlation clustering is a graph-theoretic based clustering. More precisely, in a graph G = (V,E) vertices of the graph denote the number of items, where each edge of the graph labeled as either + or - depending upon the similarity or dissimilarity...
We consider a natural generalization of both the multi-multiway cut and correlation clustering problems: the problem of multi-multiway cut with edge labels. The input to the problem is an undirected graph G=(V, E) with real nonnegative edge weights and k sets S1, S2, ..., Sk of vertices, where each edge (u, v) is labeled either + or - depending on whether u and v have been deemed to be similar or...
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