The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Firstly, according to the Hadoop platform the novel data-analysis architecture is designed, then the paper builds the Item-based clustering collaborative filtering algorithm based on Hadoop. And it takes advantage of the MapReduce parallel programming model to improve the traditional collaborative filtering recommendation algorithm, and resolves the problems of poor system performance of traditional...
In big data analytics, clustering plays a fundamental and decisive role in supporting pattern mining and value creation. To help improve user experience and satisfaction level of clustering algorithms, one important key is to let users define the quality of the aggregated clusters (e.g. In terms of the homogeneity and the relative population of each resulting cluster) they prefer instead of to fix...
Clustering is an important technique for intelligence computation such as trust, recommendation, reputation, and requirement elicitation. With the user centric nature of service and the user's lack of prior knowledge on the distribution of the raw data, one challenge is on how to associate user quality requirements on the clustering results with the algorithmic output properties (e.g. number of clusters...
Data gathering is one of the most important operations in wireless sensor networks. Since the nodes operate on limited power, it is a critical task to design an energy-efficient data gathering algorithm. In this paper, we propose an energy-efficient data gathering algorithm (EDGA) in which the network is grouped into clusters (each with a clusterhead) and the nodes form chains in each cluster. Firstly,...
Aiming at the drawback of being easily trapped into the local optima and premature convergence in quantum-behaved particle swarm optimization algorithm, clustering coefficient and characteristic distance is proposed to measure diversity of the population by which quantum-behaved particle swarm optimization algorithm is guided. The population is divergent to increase population diversity and enhance...
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
For densely deployed wireless sensor networks, there is a high correlation between multiple sensors in close proximity. In this paper, a data collection algorithm for clustered large-scale wireless sensor networks is proposed. The cluster heads fuse the data of cluster members by polynomial fitting, and transfer the fitting coefficients to base station or other cluster heads. Based on the locations...
In recent years, multi-class SVM has been one of the hot spots for many researchers, and multi-class classification based on clustering is one of strategies. Because the information of class-labels is not considered by clustering, too much branches of the binary-tree are formed, especially in the case of samples in different classes having similar features. To solve the problem, linear discriminant...
High-dimensional feature space affects the quality and efficiency of text categorization. This paper investigates an improved genetic algorithm that how to help select relevant features in text classification. We follow the so-called "region growing" method to initialize the population, and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.