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181. Based on root tree and Maximal Weighted Independent Set, we propose the EACT(Energy-Aware Clustering Tree) algorithm and the maintenance and update algorithm of the cluster tree backbone network.

182. Introduced the concept of edge clustering coefficient (ECC) to community structure discovery of simple graph and proposed an algorithm of community discovery based on ECC.

183. Secondly, by analyzing the characteristic of ants system, we make ants connect the similar data. We advance an adaptive ant clustering algorithm based on digraph(A3CD) which is more direct and easier.

184. According to fuzzy clustering theory and fuzzy pattern recognition theory, a theory and model deciding forecast factor weight was present on basis of fuzzy object function in this paper.

185. The results of clustering analysis suggested that OCD and obsessive-compulsive personality disorder were closely linked.

186. This paper also develops a fuzzy competitive learning scheme for these new reference vector parameters, and applies the algorithm to the difficult task of clustering documents.

187. This paper introduces a text clustering method based on VSM ( vector space model ).

188. In this article we will apply clustering and outlier detection method on data of tax.

189. Clustering in AS5 supports buddy replication for SFSBs for improved scalability and passivation for clustered web sessions to control memory usage.

190. Shannon's mutual information and fuzzy entropy clustering based oil analysis method was presented.

191. This paper proposes a bidirectional hierarchical clustering algorithm of simultaneous clustering words of different categories.

191. Wish you will love sentencedict.com and make progress everyday!

192. The spatial clustering analysis is a method of the spatial data mining. The spatial clustering analysis can directly find some useful clustering structure from spatial database.

193. Clustering analysis is important part of data mining. It is an unsupervised learning process and it doesn't need prior knowledge about data set.

194. This paper reconstructs the phase space based on stock yield sequence. We use WaveCluster method to mine temporal patterns by taking cumulative yield and density as clustering measure.

195. We explore the clustering characteristics on xanthous face, and point out that HSV color model can be well used to express the clustering characteristics of xanthous faces.

196. Clustering or segmentation of data is an important data mining application.

197. Application of fuzzy clustering analysis is investigated for the structure identification of fuzzy neural network.

198. It decreases clustering efficiency in evidence and results in hugeness dataset while centralize all the local data.

199. Traditional fuzzy text clustering methods mostly get the fuzzy equivalent matrix or fuzzy division by iterating the matrix of membership degree. Huge storage space is necessary for that process.

200. This clustering algorithm can deal with network intrusion detection from mass unlabeled data(sentencedict.com), distinguish between normal and abnormal data and detect unknown attacks.

201. If clustering text, you need to convert the text to a numeric representation.

202. After analyzing speedup and communication scheme theoretically parallel clustering algorithm , named PARCLE, is developed.

203. In the near future at clustering periodic time series data study up have more and more extensive trend.

204. This clustering makes each of us more productive, which in turn makes the place we inhabit even more so—and our collective creativity and economic wealth grow accordingly.

205. This paper formulates for solving the data clustering problem as a graph partition optimal problem, and proposes a parthenogenetic algorithm based on immune mechanism.

206. Aiming to some flaw, people bring forward to use Self-Organizing Feature Map on collecting data to make clustering and watching at first, and obtain principium information about some collection data.

207. This paper proposes CF - WFCM algorithm including feature weight learning algorithm and clustering algorithm.

208. The system applies conceptual clustering technology to recognize outliers, and uses plan recognition and causal network to predict and recognize the target.

209. With respect to multiple attribute clustering analysis problems with linguistic assessment information, a new maximal tree clustering analysis method is proposed.

210. Distance-based and density-based clustering algorithms in Euclidean space can be used in this framework.