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In traditional Gene Expression Programming (GEP), individuals' survival too much depends on fitness while their relationships are ignored. Borrowing the idea from the minority protection in real life, this study introduces a novel Cluster Delegate algorithm (CDA) and makes the following contributions: (1) propose several new concepts including individual similarity, ??- cluster, and the farthest neighborhood...
The traditional clustering algorithms are only suitable for the static datasets. As for the dynamic and incremental datasets, the clustering results will become unreliable after data updates, and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again. To overcome these problems, a new incremental clustering algorithm is proposed on the basis of density...
With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags is avid requirement for Web2.0 application. However, no proper algorithm can tackle this task very well. This paper proposes a core-tag oriented clustering method to handle the task...
This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm - Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design...
This paper discusses the theory and algorithmic design of the CADD (clustering algorithm based on object density and direction) algorithm. This algorithm seeks to harness the respective advantages of the k-means and DENCLUE algorithms. Clustering results are illustrated using both a simple data set and one from the geological domain. Results indicate that CADD is robust in that automatically determines...
For applications of clustering algorithms, a key technique is to handle complicatedly distributed clusters effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper. Experimental results show that the algorithm can handle clusters of arbitrary shapes, sizes...
This paper presents a novel approach which incorporates dimension extension and generalized inverse transformation (DEGIT) to realize data clustering. Unlike k-means algorithm, DEGIT needs not pre-specify the number of clusters k, centroid locations are updated and redundant centroids eliminated automatically during iterative training process. The essence of DEGIT is that clustering is performed by...
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