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In traditional grid clustering algorithms, the cluster results are just consisted of dense grids so that the clustering quality is low, while these algorithms are unable to cluster the multi-density datasets. In this paper, we propose a clustering algorithm based on grid and boundary over multi-density datasets. In order to describe the data distribution, boundary grid is introduced and checked by...
Previous studies have focused on serveral aspects of CRM (Customer Relationship Management). However, there is a lack of research that focuses on the customer segmentation of shipping enterprises using data mining. Data mining technology can be used to in modern CRM to greatly enhance it function and efficiency. Based on the technologies of clustering and classification in data mining, this paper...
High performance using minimal resources has become a serious problem for digital signal processing (DSP) applications. The number of addressable registers is a significant obstacle for centralized architecture achieving high performance of DSP applications. In this paper, we propose a novel cluster based architecture synthesis algorithm, using minimal resources with time and register constraints,...
This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm - Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design...
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements,...
The traditional text clustering algorithm often uses the unsupervised feature selection method to select the feature. In this paper we propose a new text clustering algorithm SFFCM which use the supervised feature selection method to select the feature. The SFFCM is based on the EM algorithm. In the E-step, to calculate the expectation, we use the supervised feature selection algorithm to calculate...
In traditional FCM clustering algorithm each feature is supposed to have equal importance. Considering different feature with different importance, this paper presented an improved FCM algorithm with adaptive weight for features of each cluster, named AWFCM. In the iterative AWFCM process, to identify the importance of features of each cluster, the weight for feature is computed dynamically based...
In wireless sensor networks, the accuracy of data fusion is likely to be contaminated by the environment affects nearby the nodes. In this paper, an appropriate fusion model and algorithm is given to solve this problem. According to the clustering fusion model, a weighted fuzzy fusion algorithm based on fuzzy reasoning for cluster heads, is proposed. The advantage of this algorithm is to vary weight...
The World Wide Web is a major source of the news now. The news analysis play more and more important roles in the political decision, financial analysis, invest decision, market forecast and so on. This paper proposes a news analysis system based on the hall for workshop of metasynthetic engineering. In this system, in order to analyses online news, the news articles are active fetched, and being...
This paper analyzed some problems existing in the business search engine when it searched the special fields, and then put forward a set of search engine scheme about data warehouse design based on data mining. It applied the improved association rules algorithm to text clustering algorithm. At the same time in combination with the advantages of J2EE architecture in system development, this paper...
Subspace clustering has attracted great attention due to its capability of finding salient patterns in high dimensional data. Order preserving subspace clusters have been proven to be important in high throughput gene expression analysis, since functionally related genes are often co-expressed under a set of experimental conditions. Such co-expression patterns can be represented by consistent orderings...
Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods...
We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites which communicate through a routing tree. Distributed clustering on high speed data streams is a challenging task due to limited communication capacity, storage space, and computing power at each site. In this paper, we propose a suite...
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