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Our group recommender system was targeted at a scenario that requires the adoption of group recommendation techniques to conserve computational resources. The profile aggregation strategy was used in our work to implement this group recommendation system. Key to our work, user clustering is also the first step of our work. The accuracy of user clustering could be improved once we processed the data...
Information flow detection is dedicated to tracking the dynamics and evolution of Web information spreading across the entire web over time. How to choose a comfortable information granularity to detect and how to track information evolution from one to another are the main challenges. Besides, the technological problem of doing that with a large scale information efficiently is yet to be solved....
A regional autonomy control (RAC) strategy is proposed in this paper in order to solve the integration problem of massive electric vehicle (EV) clusters into future city power grids from a high vision of active distribution network (ADN), minimize the negative impacts on the existing grid, and improve the load profile. The strategy mainly consists of two parts: the power allocation algorithm that...
Idea Graph is a core component of Idea Discovery, which discovers idea by converting unstructured data into a scenario graph. Since the scenario graph is complex and heterogeneous, the layouts generated by general graph layout algorithms can't well support human cognition. To tackle this issue, a novel graph layout algorithm named CiFDAL is proposed, which is a hybrid of circular layout algorithm...
In current days, data tend to become much bigger than before, and the distributed computing system is an prevalent option to deal with them. As one of powerful tools, MapReduce framework provides a cheap and efficient way to write parallel programs to run on distributed computing systems. Chance discovery (CD) is an extension of data mining, where chance refers to rare but important events or situations...
In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph...
Internet and E-Commerce are becoming an integral part of everyday life as we accumulate more and more knowledge that demands for some personalized recommendation technology. Collaborative filtering recommendation is the most successful personalized recommendation algorithm among current technologies. The paper suggests the two-phase clustering-based collaborative filtering algorithm. which not only...
While navigation within complex information spaces is uneasy for all users, it is extremely difficult for visually impaired users who can not simply searching and browsing digital contents with a mouse. These users have to listen line by line using a screen reader program, which may be particularly inefficient in a large documents with complex structures and loose connections of relevant information...
In order to improve the efficiency of regression testing, many test selection techniques have been proposed to extract a small subset from a huge test suite, which can approximate the fault detection capability of the original test suite for the modified code. This paper presents a new regression test selection technique by clustering the execution profiles of modification-traversing test cases. Cluster...
Recently, data mining over uncertain data streams has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications. In this paper, we try to resolve the problem of clustering over uncertain data streams. Facing uncertain tuples with different probability distributions, the clustering algorithm should not only consider the tuple value...
In this paper we present a new clustering method based on K-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of K selected from the aspects of the algorithm is improved. First, the initial clustering number is radicN. Second, through the application of the sub-merger strategy the categories were combined...
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