Improving hierarchical document cluster labels through candidate term selection

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels must be built using all the terms in the documents of the collection. This paper presents the SeCLAR method, which explores the use of association rules in the selection of good candidates for labels of hierarchical document clusters. The purpose of this method is to select a subset of terms by exploring the relationship among the terms of each document. Thus, these candidates can be processed by a classical method to generate the labels. An experimental study demonstrates the potential of the proposed approach to improve the precision and recall of labels obtained by classical methods only considering the terms which are potentially more discriminative. © 2012 - IOS Press and the authors. All rights reserved.

Clustering von deutschen Ortsnamen

Diese Studie untersucht Gruppen von Ortsnamen in Deutschland (in den Postleitregionen) nach vorhandenen Ähnlichkeiten. Als Messgröße wird ein Häufigkeitsvektor von Trigrammen in jeder Gruppe herangezogen. Mit der Anwendung des Average Linkage-Algorithmus auf die Messgröße werden Cluster aus räumlich zusammenhängenden Gebieten gebildet, obwohl das Verfahren keine Kenntnis über die Lage der Cluster zueinander besitzt. In den Clustern werden die zehn häufigsten n-Gramme ermittelt, um charakteristische Wortpartikel darzustellen. Die von den Clustern umschriebenen Gebiete lassen sich zwanglos durch historische oder linguistische Entwicklungen erklären. Das hier verwendete Verfahren setzt jedoch kein linguistisches, geographisches oder historisches Wissen voraus, ermöglicht aber die Gruppierung von Namen in eindeutiger Weise unter Berücksichtigung einer Vielzahl von Wortpartikeln in einem Schritt. Die Vorgehensweise ohne Vorwissen unterscheidet diese Studie von den meisten bisher angewendeten Untersuchungen.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

HCX : an efficient hybrid clustering approach for XML documents

This paper proposes a novel Hybrid Clustering approach for XML documents (HCX) that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The empirical analysis reveals that the proposed method is scalable and accurate.