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The rapid development of information technologies give rise to the big data era. In this age, large amounts of unlabeled software defect metric data at a significantly lower cost is collected. It is how to exploit the unlabeled data to predict software defect has become a hot topic during the past few years. In this paper, a novel method called chaotic and immune spectral clustering (CISC) is proposed...
Real-time crash prediction models are playing a key role in transportation information system. Support vector machine (SVM), a classification learning algorithm, was introduced to evaluate real-time crash risk. The size of traffic dataset is always large with a high accumulating speed. By applying a warm start strategy, an incremental learning algorithm is introduced to update the original model....
With the rapid development of radio service and monitoring facilities, radio monitoring application steps into big data era. Big data analysis technology can help us get valuable information through dealing with massive monitoring data, which offer guidance to wireless spectrum resource management, abnormal signal detection, etc. In this paper, we adopt association rule mining algorithm to realize...
In order to improve the efficiency and adaptability of classical random forest algorithm in large data environment, an improved random forest algorithm based on Spark is proposed. Firstly, an improved random forest algorithm (FRF) based on the Fayyad boundary point principle is proposed to deal with the shortcomings of classical random forest algorithm in the process of discretization of continuous...
User-item rating data preprocessing is an important factor that influences the accuracy of the collaborative filtering algorithms. When users assign a rating to an item, the rating may be influenced by some external factors, such as users' emotional factor. By analyzing the deviation of the users' ratings, this paper presents a novel recommendation method based on adjusted user-item rating matrix...
Trust, as the basis of human interactions, has been playing an important role in addressing information sharing, experience communication, and public opinions. Trust-aware recommender systems are an effective solution to the information overload problem, especially in the online world where we are constantly faced with inordinately many choices. In this paper, to build a trust-aware recommender system...
With the deepening development of complex network, it is severe for detection requirement to know network structure gradually, which the community detection algorithm has been proposed and deeply improved. In view of the high space-time complexity of network divisive and agglomerative algorithms, and the uncertainty of clustering algorithms for many relative sparse networks, a non-overlapping algorithm...
Friend recommendation has been one of the most challenging problems as the social networks grow rapidly, due to the needs of seeking people who are acquaintances in real life or share the common interests. In this paper, we tackle the problem by treating it as a link prediction task and propose a hybrid algorithm that exploits the existing friendship links, users' history ratings and the tags annotated...
First, according to characteristics of mobile social environment, by using optimization models based on similarity degree and interaction degree respectively, the optimal correlated users can be selected for analyzing two main factors of a target user's behaviors (i.e. long-term habits and short-term influences); furthermore, an adaptive update strategy based on fuzzy theory is proposed to describe...
In the big data era, the next generation optical networks technology for big data has attracted more and more attention, with increasing challenges of optical networks. In the OpenFlow controlled optical network, it is a great problem to predict long-term traffic characteristics only according to short-term traffic characteristics. And it may fail to actively support diversified services. Thus, the...
In this work, we propose a method collaborating the local similarity and local community paradigm with a tunable parameter to balance the contribution of the energy from these two sources. We show that local similarity e.g., common neighbors and local community paradigm e.g., local community links both play significant roles in network evolution; therefore, one cannot ignore or penalize anyone of...
Aiming at the application requirements for the frequent handover in 5G Ultra Dense Networks (UDN), a scheme of terminal mobility prediction for UDN based on Support Vector Machine (SVM) is proposed. In view of the characteristics of density and mobility in UND, the schema uses SVM to mine the vector index of the mobile terminal and adopts regression algorithm to predict the location of the terminal...
With the rapid development of Cloud Manufacturing technology, the number of services with the same or similar functions have emerged greatly on the platform. The existing research of predicting execution time of manufacturing cloud services is relatively few and the service execution time is mostly estimated by the average of historical executions. However, execution time changes dynamically in the...
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