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Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm...
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster...
Clustering is considered as the most important unsupervised learning problem. It aims to find some structure in a collection of unlabeled data. Dealing with a large quantity of data items can be problematic because of time complexity. On the other hand high dimensional data is a challenge arena in data clustering e.g. time series data. Novel algorithms are needed to be robust, scalable, efficient...
Hierarchical clustering is one of the most important tasks in data mining. However, the existing hierarchical clustering algorithms are time-consuming, and have low clustering quality because of ignoring the constraints. In this paper, a Hierarchical Clustering Algorithm based on K-means with Constraints (HCAKC) is proposed. In HCAKC, in order to improve the clustering efficiency, Improved Silhouette...
In view of ignoring semantic relationship between words, high dimensionality of data and computational complexity when current text clustering algorithms deal with Chinese texts. This paper presents a new method to cluster Chinese texts based on semantics in a specific field-TCBS (Text Clustering Based on Semantics) algorithm. The algorithm is based on the agglomerative hierarchical clustering algorithm,...
The clustering agglomerative hierarchical algorithm for date grouping is considered. To reduce algorithmic complexity without accuracy losses an approach with the speed and accuracy coefficient is proposed. Some results with quality characteristics of clustered data are presented.
There are a large quantity of non-certain and non-structure contents in the Web text at the present time. It is difficult to cluster the text by some normal classification methods. An algorithm of Web text clustering analysis based on fuzzy set is proposed in this paper, and the algorithm has been described in detail by example. The technique can improve the algorithm complexity of time and space,...
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