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In traditional e-commerce websites, social tags are used in product classification only, and not applied in the domain of personalized recommendation technology. In this paper, we propose a personalized recommendation model based on social tags. We build a user interest model for products by reflecting user interest and product features directly through social tags, and optimize the interest model...
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...
Researched on the characteristic of the traffic flow, a new method of abnormal data detection on traffic flow based on the curve-fitting is presented. First, the historical data on traffic flow are divided into the traffic flow with high speed and the traffic flow with low speed by the clustering analysis. Then, the algorithm on the determination safety zone scope is obtained by the curve-fitting...
Searching initial centers in high dimensional space is an interesting and important problem which is relevant for the wide various types of K-Means algorithm. However, this is a very difficult problem, due to the"curse of dimensionality"and the inherently sparse data.Algorithm IMSND is one of the latest initialization methods that are based on the idea of sharing neighborhood density. Concerning...
In order to make the enterprise develop better and faster, It's important that making a prediction on the enterprise's development condition by using information fusion technology, which can make prewarning about the recessive existing problems in time. In the paper, clustering fusion algorithm and its improved algorithm were analyzed, and were applied in the prewarning system of enterprise's operation,...
To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database...
Fuzzy C-means (FCM) and Rough K-means (RKM) algorithms are two popular soft clustering algorithms that allow for overlapping clusters. The overlapping clusters can be useful in applications where restrictions imposed by crisp clustering that force assignment of every object to a unique cluster may not be practical. Likewise RKM and FCM, interval set representation of clusters would also generate overlapping...
Co-occurrence data matrices arise frequently in various important applications such as a document clustering. By considering a multinomial mixture model, we present a new probabilistic Self-Organizing Map (SOM) for clustering and visualizing this kind of data. Contrary to SOM, our proposed learning algorithm optimizes an objective function. Its performances are evaluated by using Monte Carlo simulations...
In data grids, the fast and proper replica selection decision leads to better resource utilization due to reduction in latencies to access the best replicas and speed up the execution of the data grid jobs. In this paper, we propose a new strategy that improves replica selection in data grids with the help of the reduct concept of the Rough Set Theory (RST). Using Quickreduct algorithm the unsupervised...
The biclustering problem consists in simultaneously clustering rows and columns of a data matrix. The aim of this paper is to empirically assess the performance of cooperative coevolution as an alternative approach for coping with the task of discovering good and sizeable biclusters. For this purpose, two cooperative coevolutionary algorithms, one configured with genetic algorithms (GAs) and another...
An improved moving object segmentation approach which extracted motion field from H.264 compressed domain is proposed. Pre-treatments such as vector median filtering and forward block vector accumulation are used to obtain more obvious motion field. Then mix and hierarchical clustering algorithm based on improved k-means and EM is exploited to segment the moving on the macro-block level and on the...
To reduce energy consumption and prolonging the lifetime of wireless sensor networks, effective methods are needed. In this paper, a hierarchical and multi-hop clustering algorithm is proposed to increase the lifetime of wireless sensor networks. This algorithm selects two cluster-heads for each cluster. In this algorithm, one of the cluster-heads is responsible for data collection, data aggregation...
This paper presents a target classification based on fuzzy equivalence relation. Firstly, utilizing improved six invariant moments obtain the objective characteristic matrix. Secondly, a kind of feature weighting function is proposed, making the data classification more effective. Then a fuzzy equivalence relation based on weighted feature matrix can be constructed. Finally, using F statistic to determine...
Parallel computing is the use of multiple compute resources to solve a computational problem. Parallel computers can be roughly classified as Multi-Core and Multi-Processor. In both these classifications, the hardware supports parallelism with computer node having multiple processing elements in a single machine. Parallel programming is the ability of program to run on this infrastructure which is...
As there still isn't any theoretical foundation or effective evaluation for the definition of fuzzy weighting exponent at present, a kind of evolutionary algorithm of fuzzy weighting exponent based on subset measuring is presented during the application of Fuzzy C-Means (FCM). Firstly, a clustering validity function is defined based on subset measuring theory, then the effectiveness of clustering...
Most real-world data sets are characterized by a high dimensinal, inherely sparse data space. In this paper, we present a novel density-based approach to the subspace clustering problem. A new framework for data stream mining is introduced, called the weighted sliding window. In the online component, the structure of Exponential Histogram of Cluster Feature(EHCF) is improved to maintain the micro-clusters...
Advantages of None Euclidean Relational Fuzzy C-means (NERFCM) is analysed, by which four Fuzzy C-means (FCM) clustering algorithms are compared, which includes Fuzzy C-means (FCM) and traditional Relational Fuzzy C-means (RFCM) and None Euclidean Relational Fuzzy C-means (NERFCM) and Any Relational Fuzzy C-means (ARFCM). Their common points and different limitations on usage are discussed, finally...
In order to resolve the current problem about seriously academic plagiarism in the web environment, this article proposes an algorithm of the text copy detection on the topic bag and the algorithm uses the idea of semantic clustering and multi-instance learning. Firstly, a paper is divided into three layers construction tree: a leaf node denotes a sentence; a branch node represents a topic bag, and...
The application of fuzzy c-means algorithm to image segmentation is not taking into account spatial information apart from intensity values , which will lead a misclassification on the boundaries and inhomogeneous regions with noises.In order to solve this problem, a new image segmentation method is proposed using adaptive spatially median neighborhood information and fuzzy c-means algorithm in this...
Data clustering is an effective method for data analysis and pattern recognition which has been applied in many fields such as image segmentation, machine learning and data mining. It is the process of splitting the multidimensional data into several groupings or clusters based on some similarity measures. A cluster is usually defined by a cluster center. Generally, the information of the features...
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