Parallel Algorithm for Extended Star Clustering

Gil-García, Reynaldo; Badía-Contelles, José Manuel; Pons-Porrata, Aurora

doi:10.1007/978-3-540-30463-0_50

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…A different version of the Extended Star algorithm was proposed by Gil et al to construct a parallel approach [7]. This new version is also independent of data order, and solves the first drawback of the former Extended Star algorithm, but it can produce unnecessary clusters and illogical (less dense) clusters.…”

Section: Related Workmentioning

confidence: 99%

“…The Extended Star method outperforms the original Star algorithm, reducing considerably the number of clusters; nevertheless this algorithm can leave uncovered objects and in some cases produce unnecessary clusters. Another version of the Extended Star method was proposed by Gil et al to construct a parallel algorithm [7]. However, this version also has some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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ACONS: A New Algorithm for Clustering Documents

Gago-Alonso

Suárez

Pagola

Lecture Notes in Computer Science

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In this paper we present a new algorithm for document clustering called Condensed Star (ACONS). This algorithm is a natural evolution of the Star algorithm proposed by Aslam et al., and improved by them and other researchers. In this method, we introduced a new concept of star allowing a different star-shaped form; in this way we retain the strengths of previous algorithms as well as address previous shortcomings. The evaluation experiments on standard document collections show that the proposed algorithm outperforms previously defined methods and obtains a smaller number of clusters. Since the ACONS algorithm is relatively simple to implement and is also efficient, we advocate its use for tasks that require clustering, such as information organization, browsing, topic tracking, and new topic detection.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ACONS: A New Algorithm for Clustering Documents

Gago-Alonso

Suárez

Pagola

Lecture Notes in Computer Science

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“…Some examples of this kind of applications are information retrieval [1], social network analysis [2], text segmentation [3], among others. In the literature, several algorithms have been proposed for overlapping clustering [4,5,6,7,8,9,10,11], which are different according to their mathematical basis and clustering strategies, as well as the type of datasets they can process. Due to their simplicity, the Kmeans algorithm [12] together with its variants are clustering algorithms that have been widely used in several applications.…”

Section: Introductionmentioning

confidence: 99%

Study of Overlapping Clustering Algorithms Based on Kmeans through FBcubed Metric

Aroche-Villarruel

Carrasco-Ochoa

Martínez-Trinidad

et al. 2014

Lecture Notes in Computer Science

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In this paper we present a study of the overlapping clustering algorithms OKM, WOKM and OKMED, which are extensions to the overlapping case of the well known Kmeans algorithm proposed for building partitions. Different to other previously reported comparisons, in our study we compare these algorithms using the external evaluation metric FBcubed which takes into account the overlapping among clusters and we contrast our results against those obtained by F-measure, a metric that does not take into account the overlapping among clusters and that has been previously used in another reported comparison.

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“…Another version of the Extended Star algorithm was proposed by Gil et al to construct a parallel algorithm [8]. Nevertheless, this version also has the same drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A New Graph-Based Algorithm for Clustering Documents

Suárez

Trinidad

Carrasco-Ochoa

et al. 2008

2008 IEEE International Conference on Data Mining Workshops

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In this paper a new algorithm, called CStar, for document clustering is presented. This algorithm improves recently developed algorithms like Generalized Star (GStar) and ACONS algorithms, originally proposed for reducing some drawbacks presented in previous Star-like algorithms. The CStar algorithm uses the Condensed Star-shaped Subgraph concept defined by ACONS, but defines a new heuristic that allows to construct a new cover of the thresholded similarity graph and to reduce the drawbacks presented in GStar and ACONS algorithms. The experimentation over standard document collections shows that our proposal outperforms previously defined algorithms and other related algorithms used to document clustering.

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Parallel Algorithm for Extended Star Clustering

Cited by 7 publications

References 6 publications

ACONS: A New Algorithm for Clustering Documents

ACONS: A New Algorithm for Clustering Documents

Study of Overlapping Clustering Algorithms Based on Kmeans through FBcubed Metric

A New Graph-Based Algorithm for Clustering Documents

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