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1 The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.

2 The shortcomings about these days clustering algorithm considering the condition of planar adjacency relationship are analysised.

3 A multi-level document clustering algorithm was proposed based on association rules, It constructed document feature vector of topic and keyword by using a new method of document feature extraction.

4 Fuzzy c - Mean ( FCM ) clustering algorithm is one of the widely appliedalgorithms in unsupervised model recognition fields.

5 In this research hierarchical clustering algorithm is used with a newly defined criterion function, and it can cluster data without known cluster number.

6 This paper introduces fuzzy clustering algorithm into analyzing software metrics data.

7 An adaptive clustering algorithm, based on a combination of self-generating neural network(SGNN) with improved genetic algorithms(IGAs), is proposed for dermoscopy melanoma image in this paper.

8 A new method based on clustering algorithm is presented to detect anchorperson shot in news video.

9 We put clustering algorithm into T-S fuzzy neural networks and withdraw system's character, optimize the input space in this method in order to set up a self-adaptive model.

10 Wireless sensor networks LEACH hierarchical clustering algorithm for simulation of source code.

11 To improve efficiency of density-based clustering algorithms and deal with the constrains of non-spatial attributes, a novel spatial clustering algorithm called GDRS is proposed.

12 A fast Scan Line Seed Fill based Clustering Algorithm for Two-value Image is proposed in this paper.

13 Experiment results showed that the accuracy of this clustering algorithm was significantly higher than unweighted fuzzy C-means clustering algorithm.

14 The application of Rough Set theory in designing clustering algorithm is discussed in the second section.

15 Therefore, this article applies suffix tree algorithm to the meta search engines, and designs a meta search engine systems with a clustering algorithm.

16 Aiming at Chinese message text clustering, this paper proposes a clustering algorithm based on semantic.

17 In view of above questions, a probability - density - based data stream clustering algorithm was proposed.

18 In order to analyze the structure of a dataset simply and efficiently, this paper proposes a new clustering algorithm based on minimal spanning tree: MSTCA.

19 To automatically acquire the fuzzy space structure of system and the number of fuzzy rules, the ant colony clustering algorithm is used in Structure identification.

20 In order to cluster multi - density dataset, a clustering algorithm on density - reachable for multi - density is proposed.

21 Facing the fact that the Chinese training corpus for coreference resolution is heavily lacking, this paper presents a new unsupervised clustering algorithm for noun phrase coreference resolution.

22 Finally,[www.Sentencedict.com] a numerical example shows the applicability of the FCM clustering algorithm.

23 The main research content includes the following four parts:(1) Aiming at the problems existing in MHS, stable multi-layer hierarchical structure (SMHS) and its clustering algorithm are proposed.

24 The coding time is reduced greatly by using PCC clustering algorithm, taking advantage of nearest-neighbor correlation and threshold of matching error.

25 This paper mainly studies the technology of data mining for compressed data, including association rule mining, classification and clustering algorithm.

26 For the first weakness, this paper proposes an improved possibilistic clustering algorithm with a mutual repulsion of the clusters, whose parameters can easily be handled.

27 In the case of detection of hyperspherical-shaped objects, this clustering algorithm is much better than traditional algorithms.

28 This paper has proposed and realized a kind of text clustering algorithm used for high dimensional sparse similar matrix.

29 Feature weighting and sample weighting are considered in this hybrid clustering algorithm and the cluster number is automatically set by using the cluster validity index to finish clustering.

30 The Euclidean distance is usually chosen as the similarity measure in the conventional K-means clustering algorithm, which usually relates to all attributes.