Detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data. The police records exist in various formats and the quality of analysis greatly depends on the background knowledge of the analyst. This paper proposes a simple correlation clustering algorithm which aims at finding illegal activities of professional identity fraudsters based...

Modern day science presentsmany challenges to data analysts. Advances in data collection provide very large (number of observations and number of dimensions) data sets. In many areas of data analysis an informative task is to find natural separations of data into homogeneous groups, i.e. clusters. In this paper we study the asymptotic behavior of hierarchical clustering in situations where both...

Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints – must-link and cannot-link constraints – and show how they can be incorporated into a clustering algorithm to aid that search. For three of the four data sets tested, our results indicate that the incorporation of surpris...

A method is proposed for detecting the emotions of Chinese song lyrics based on an affective lexicon. The lexicon is composed of words translated from ANEW and words selected by other means. For each lyric sentence, emotion units, each based on an emotion word in the lexicon, are found out, and the influences of modifiers and tenses on emotion units are taken into consideration. The emotion of ...

The area of clustering with constraints makes use of hints or advice in the form of constraints to aid or bias the clustering process. The most prevalent form of advice are conjunctions of pair-wise instance level constraints of the form must-link (ML) and cannot-link (CL) which state that pairs of instances should be in the same or different clusters respectively. Given a set of points P to cl...

We consider the problem of constructing an an optimal-weight tree from the 3 `

A major problem encountered by text clustering practitioners is the difficulty of determining a priori which is the optimal text representation and clustering technique for a given clustering problem. As a step towards building robust document partitioning systems, we present a strategy based on a hierarchical consensus clustering architecture that operates on a wide diversity of document repre...

In this paper I’ll speak about non-spectral clustering techniques and see how a node ordering based on centrality measures can improve the quality of communities detected. I’ll also discuss an improvement to existing techniques, which further improves modularity.

Work on rst-order clustering has primarily been focused on the task of conceptual clustering, i.e., forming clusters with symbolic generalizations in the given representation language. By contrast, for propositional representations, experience has shown that simple algorithms based exclusively on distance measures can often outperform their concept-based counterparts. In this paper, we therefor...

The correlation clustering is an NP-hard problem, hence its solving methods do not scale well. The contraction method and its improvement enable us to construct a divide and conquer algorithm, which could help us to clustering bigger sets. In this article we present the contraction method and compare the effectiveness of this new new and our old methods.