Yangtao Wang

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As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in the unlabelled data. Clustering algorithms that need to store the entire data into the memory for analysis become infeasible when the data set is too large to be stored. To handle such kind of large data, incremental(More)
Data is growing at an unprecedented rate in commercial and scientific areas. Clustering algorithms for large data which require small memory consumption and scalability become increasingly important under this circumstance. In this paper, we propose a new clustering approach called stochastic gradient based fuzzy clustering(SGFC) which achieves the(More)
The development of an amphibian crab-like robot prototype is proposed according to the need of duty under bad circumstance. It can be used as the carrier of the reconnaissance equipment, the weapon system and the communication system, and complete many kinds of missions near the sea and tideland where common soldiers can not complete the mission. The(More)
Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features(More)
Recently, an attractive clustering approach named multiexemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar-based AP. MEAP is able to automatically identify multiple exemplars for each cluster associated with a superexemplar. However, if the cluster number is a prior knowledge and can be specified by the user, MEAP(More)
Clustering is an important unsupervised technique of data analysis to find the underlining information of the unlabelled data. Many clustering approaches have been developed and reported in the literature and some of them are widely applied in real world problems such as k-means and fuzzy k-means. However, when handling imbalanced data in which the classes(More)
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the(More)
Mining valuable information and knowledge in the prevalent large data nowadays is crucial to gain competitive advantages for many parties. Clustering is an important technique for data analysis to find the natural distribution of the unlabelled data. Clustering algorithms need to store the entire data into memory for analysis become infeasible when the data(More)
Recently, an attractive clustering approach named multi-exemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar based Affinity Propagation (AP). MEAP is able to automatically identify multiple exemplars for each cluster associated with a super exemplar. However, if the cluster number is a prior knowledge and can be(More)