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Notice of Violation of IEEE Publication Principles"A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining"by Xiao-Li Dong, Cheng-Kui Gu, Zheng-Ou Wangin the Proceedings of the Fifth International Conference on Machine Learning and CyberneticsAfter careful and considered review of the content and authorship of this paper by a duly constituted expert...
Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation...
From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set. This K-means algorithm has been modified in many ways to get the modified K-means...
Most of existing fuzzy clustering approaches cluster objects based on the vector representation or their pairwise relation. In this paper, we propose a new approach called LinkFCM to make use of both types of data by adding an additional term into fuzzy c-means type objective functions. This new term measures the total within cluster association. The LinkFCM is useful for clustering many real-world...
In the field of cluster analysis, most clustering algorithms consider the contribution of each attribute for classification uniformly. In fact, different attributes (or different features) should be of different contribution for clustering result. In order to consider the different roles of each attribute, this paper proposes a new approach for clustering algorithms based on weights, in which decision...
The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising...
Manifold clustering is a widely used techniques in pattern recognition and machine learning. It partition a set of input data into several clusters each of which contains data points from a separate, simple low-dimensional manifold. In order to cluster manifold, we propose a novel distance measure based on topology structure that can efficiently represent the underlying manifold. Under this distance...
In order to reduce dimension number of feature space and improve clustering precision, a novel SOM clustering algorithm based on feature selection-FSSOM is provided in this paper. This algorithm first evaluates importance and distinguishing ability of each feature, and only selects features which can efficiently improve clustering precision to construct feature space. Then, it computes kullback-leibler...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
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