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In this paper, we propose to learn LIfestyles of mobile users via mobile Phone Sensing (LIPS), and we develop a system and algorithms to realize this idea. First, we present the workflow and architecture of our system, LIPS. Combining both unsupervised and supervised learning, we propose a hybrid scheme for lifestyle learning, which consists of two parts: characterization and prediction. Specifically,...
When power system is subjected to severe disturbances and then will be out of step. The interconnected power grid should be split into several islands to avoid blackout. The controlled islanding is an active control technique based on the information from Wide Area Measurement System (WAMS) to accomplish the islanding task. This paper proposes a fast islanding strategy for large power networks. Firstly,...
Near-duplicate image discovery is the task of detecting all clusters of images which duplicate at a significant region. Previous work generally take divide and conquer approaches composed of two steps: generating cluster seeds using min-hashing, and growing the seeds by searching the entire image space with the seeds as queries. Since the computational complexity of the seed growing step is generally...
Two efficient user scheduling metrics and their corresponding algorithms are proposed for uplink multiuser MIMO systems in a finite scattering environment, where users typically transmit signals while sharing some clusters (which can be trees and buildings). By utilizing statistical channel state information such as how users employ clusters (e.g., Percentage of energy carried by each cluster) and...
For the large interconnected power system, dynamic equivalence could significantly reduce the computing load and manifest the dominant characteristics. Coherent method is one approach of dynamic equivalence and its core is the automatic identification of coherency. Based on the angle perturbed trajectory measured by WAMS, a new coherency recognition method based on Independent Component Analysis is...
Cloud radio access network (C-RAN) is a new concept of network architecture, which brings a technical revolution into the wireless communication market and leads to some kind of all new mode of the future wireless communications. In this paper the clustering algorithm based on multi-objective optimization is investigated. The proposed algorithm aims at maximizing the throughput contribution of the...
Shape features are one of the most popular low-level image representations for computer vision (CV) tasks such as template matching, image collaboration and object recognition. In this paper, an application-originated research has been introduced for extracting representative shape characteristics from challenging real-world scenes based on the image “textures”. The proposed new approach starts from...
Clustering of entity pairs is the core content of the unsupervised relation extraction method. However, most of the clustering algorithm in the previous unsupervised relation extraction does not take into account the influence of the duality between entity pairs and the relationship characteristics on clustering results. In order to overcome this defect, this paper proposed a novel clustering algorithm...
K-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in computer vision community. Traditional k-means is an iterative algorithm — in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center. The cluster re-assignment step becomes prohibitively expensive when the number of data points and cluster centers are...
Text feature is usually expressed as a matrix of huge dimensionality in text mining, and common clustering algorithm are not stable and cannot obtain clustering solution efficiently. Latent Semantic Analysis can reduce dimensionality effectively, and emerges the semantic relations between texts and terms. Clustering ensemble can get better clustering solution than single clustering method. A text...
This paper presents a weighted clustering algorithm based on the physical attraction model, which improves the physical attraction model by assigning a different weight to the position points on a GPS trace for a fast convergence according to their velocity and directional changes. The physical attraction model pulls together traces that belong on the same road in response to simulated potential energy...
In recent years, extensive researches have been conducted to develop approaches to answer two major challenges for collaborative filtering problems, namely sparsity and scalability. In this paper, we propose a novel collaborative filtering recommendation approach to alleviate these challenges. Our approach firstly converts the user-item ratings matrix to user-class matrix, and hence increases greatly...
With the widespread of Internet application, more and more enterprises build their Web sites and provide business information through Web pages. Web page classification could be used to assign the enterprise Web pages to one or more predefined business categories. On the purpose of Internet-based enterprises administration in E-government system, algorithms and application related to web page classification...
In an increasingly competitive market, the management of client relationship is becoming a key point for a enterprise to get a success in the competition, client subdivision is a foundation for the enterprise to make a precise marketing strategy and a successful management of client group, based on the development of data mining technology, a fuzzy-C-means(FCM) algorithm model is founded to do the...
This paper presents a new algorithm of Web page classification, CUCS(Combined UC and SVM), for large training set. CUCS combines the advantages of SVM (Support Vector Machine) and UC (Unsupervised Clustering), achieving high precision and fast speed. In the training stage, CUCS gets clustering centers, which include positive example centers and negative ones, by means of UC. Then CUCS prunes training...
Based on the problem of TB level mass data lacking of parallel patterns which is distributed on Earth and accessed by Internet, we focus on the research of parallel computing architecture structure--virtual cluster based on cloud computing. Meanwhile, the parallel data mining algorithm is studied, and the effectiveness of parallel data mining algorithm based on this platform is proved.
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