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Displaying 1-7 out of 7 total
SVStream: A Support Vector-Based Algorithm for Clustering Data Streams
Found in: IEEE Transactions on Knowledge and Data Engineering
By Chang-Dong Wang,Jian-Huang Lai,Dong Huang,Wei-Shi Zheng
Issue Date:June 2013
pp. 1410-1424
In this paper, we propose a novel data stream clustering algorithm, termed SVStream, which is based on support vector domain description and support vector clustering. In the proposed algorithm, the data elements of a stream are mapped into a kernel space,...
 
Incremental Support Vector Clustering
Found in: Data Mining Workshops, International Conference on
By Chang-Dong Wang,Jian-Huang Lai,Dong Huang
Issue Date:December 2011
pp. 839-846
Support vector clustering (SVC) is a flexible clustering method inspired by support vector machines (SVM). Due to its advantage in discovering clusters of arbitrary shapes, it has been widely used in many applications. However, one bottleneck which restric...
 
Kernel-Based Clustering with Automatic Cluster Number Selection
Found in: Data Mining Workshops, International Conference on
By Chang-Dong Wang,Jian-Huang Lai,Dong Huang
Issue Date:December 2011
pp. 293-299
Kernel k-means is one of the most well-known kernel-based clustering methods for discovering nonlinearly separable clusters. However, like its original counterpart k-means, kernel k-means has two inherent drawbacks: (1) it is easily trapped into degenerate...
 
A Novel Co-clustering Method with Intra-similarities
Found in: Data Mining Workshops, International Conference on
By Jian-Sheng Wu,Jian-Huang Lai,Chang-Dong Wang
Issue Date:December 2011
pp. 300-306
Recently, co-clustering has become a topic of much interest because of its applications to many problems. It has been proved more effective than one-way clustering methods. But the existing co-clustering approaches just treat the document as a collection o...
 
A Conscience On-line Learning Approach for Kernel-Based Clustering
Found in: Data Mining, IEEE International Conference on
By Chang-Dong Wang, Jian-Huang Lai, Jun-Yong Zhu
Issue Date:December 2010
pp. 531-540
Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfort...
 
NEIWalk: Community Discovery in Dynamic Content-Based Networks
Found in: IEEE Transactions on Knowledge and Data Engineering
By Chang-Dong Wang,Jian-Huang Lai,Philip S. Yu
Issue Date:July 2014
pp. 1-1
Recently, discovering dynamic communities has become an increasingly important task. Many algorithms have been proposed, most of which only use linkage structure. However, rich information is encoded in the content of social networks such as node content a...
 
Multi-Exemplar Affinity Propagation
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Chang-Dong Wang, Jian-Huang Lai,Ching Y. Suen, Jun-Yong Zhu
Issue Date:September 2013
pp. 2223-2237
The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. ...
 
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