Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface

Handl, Julia; Meyer, Bernd

doi:10.1007/3-540-45712-7_88

Cited by 112 publications

(70 citation statements)

References 9 publications

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“…Handl and Meyer [33] applied ant-based clustering as the core of a visual document retrieval system for world wide web searches in which the basic goal is to classify on line documents by contents' similarity. The authors adopted an idea of short-term memory and employed ants with different speeds, also allowing them to jump.…”

Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

“…In order to overcome this limitation, they proposed a technique which is based on the application of agglomerative hierarchical clustering method to the positions of the data items on the grid. Taking into consideration the developed method, the results achieved by the ant-based clustering algorithm proposed by Handl and Meyer [33] are compared, using both synthetic and real datasets, with those obtained by two classical algorithms (k-means and agglomerative average link), showing that the ant-based algorithm performs well when compared with them.…”

Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

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Ant-based Clustering Algorithms: A Brief Survey

Jafar¹,

Sivakumar²

2010

IJCTE

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Abstract-Ant-based clustering is a biologically inspired data clustering technique. Clustering task aims at the unsupervised classification of patterns in different groups. Clustering problem has been approached from different disciplines during last years. In recent years, many algorithms have been developed for solving numerical and combinatorial optimization problems. Most promising among them are swarm intelligence algorithms. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering techniques. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. During the last five years, research on and with the ant-based clustering algorithms has reached a very promising state. In this paper, a brief survey on ant-based clustering algorithms is described. We also present some applications of ant-based clustering algorithms.

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Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

Ant-based Clustering Algorithms: A Brief Survey

Jafar¹,

Sivakumar²

2010

IJCTE

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“…The agent is located in the centre of this neighbourhood; its radius of perception in each direction is therefore σ−1 2 . The resulting algorithm still suffers from convergence problems and an unfavourable runtime behaviour, and several attempts to overcome these limitations have therefore been proposed [6,4].…”

Section: Ant-based Clusteringmentioning

confidence: 99%

“…In this section, we build upon the work of [2,6,4] to develop a general and robust version of ant-based clustering. In particular, we describe how parameter settings for the algorithm can be automatically derived from the data, and we introduce a number of modifications that improve the quality of the clustering solutions generated by the algorithm.…”

Section: Our Algorithmmentioning

confidence: 99%

“…This saves computations in the first stage of the clustering process and prevents difficulties with the initial cluster formation. At the same time it accelerates the dissolution of preliminary small clusters, a problem that has already been addressed in [6,4]. In the current implementation, we start with an initial perceptive radius of 1 and linearly increase it to be 5 in the end.…”

Section: Increasing Radius Of Perceptionmentioning

confidence: 99%

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Strategies for the Increased Robustness of Ant-Based Clustering

Handl

Knowles

Dorigo

2004

Engineering Self-Organising Systems

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Abstract. This paper introduces a set of algorithmic modifications that improve the partitioning results obtained with ant-based clustering. Moreover, general parameter settings and a self-adaptation scheme are devised, which afford the algorithm's robust performance across varying data sets. We study the sensitivity of the resulting algorithm with respect to two distinct, and generally important, features of data sets: (i) unequal-sized clusters and (ii) overlapping clusters. Results are compared to those obtained using k-means, one-dimensional self-organising maps, and average-link agglomerative clustering. The impressive capacity of ant-based clustering to automatically identify the number of clusters in the data is additionally underlined by comparing its performance to that of the Gap statistic.

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Clustering web search results using fuzzy ants

Schockaert

Cock

Cornelis

et al. 2007

Int. J. Intell. Syst.

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Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant-based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering.

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Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface

Cited by 112 publications

References 9 publications

Ant-based Clustering Algorithms: A Brief Survey

Ant-based Clustering Algorithms: A Brief Survey

Strategies for the Increased Robustness of Ant-Based Clustering

Clustering web search results using fuzzy ants

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