Hierarchical algorithms can be either agglomerative or divisive, that is top-down or bottom-up. Allagglomerative hierarchical clustering algorithmsbegin with each object as a separate group. These groups are successively combined based on similarity until there is only one group remaining or a specified...
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Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not...
The clustering phase is progressively passing all input samples of TF matrix and are further processed using the PART neural network. As a result of processing, projective clusters are obtained. In the last phase of processing our propose algorithm based on similarity measure is used in order to...
This paper presented a method for time series clustering based on the spectral bisection method of Normal matrix. The algorithm transformed time series data into vector forms firstly, calculated the similarity between any pairs of time series and constructed complex network. Then the complex network ...
Among the techniques based on similarity functions we can include K-nearest neighbor and K-means clustering (Kumar and Toshniwal, 2015a; Zheng et al., 2014). In the case of cluster techniques whose similarity function is based on distribution probabilities, their operation is based on the ...
Spectral clustering is a clustering algorithm based on graph theory, in which the characteristic space is calculated by constructing similarity matrix, so the definition of similarity is crucial to the performance of the algorithm. How to select the appropriate matrix parameter measure in the algorithm...
This paper presents a hierarchical clustering algorithm aiNHA based on the aiNet model which is an important model of Artificial Immune System.Firstly,it generates the memory matrix and the similarity matrix of the antibodies in the aiNet method.So it can divide the data set into several sub-...
matrix is then used to derive all the edge weights for a graph, in which each sequence is represented by a vertex, and the edge weight for an edge denotes the pairwise similarity between the pair of sequences associated with. Sequence clustering is now performed via an iterative graph ...
, and a similarity matrix , RCC algorithm objective is to learn a representation matrix , the objective function means that :The sample point and its representative point must be similar. The representative point needs to represent the characteristics of the original sample point. At the same time...