Sequential pattern mining has a wide range of applications in data streams. The real data involves multiple data streams and each data stream is itemset-sequence. However, most algorithms mine a single item in a singl...
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Software behavior mining is a very meaningful work. Finding that desirable patterns can assist the program maintainers to comprehend the software adequately. Although the existing high utility pattern mining algorithm...
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Maximal frequent sequence mining is an important research issue which has realized the highly compressed storage of frequent sequences. At present, most algorithms are based on bottom-up method and large numbers of ca...
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In this paper, we proposed a new sequential pattern mining algorithm called WSPD for mining weighted sequential patterns in data streams. The algorithm produces no false negatives and places a bound on the error of th...
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With the explosive growth of wireless application, how to improve the spectrum efficiency as well as reduce the communication consumption is a hot topic of research. In this paper, we propose a novel energy saving str...
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It is a fundamental issue to find a small subset of influential individuals in a complex network such that they can spread information to the largest scope of nodes in the network. Informative functions in complex sof...
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According to the former research achievements about load balancing strategy, considering the load distribution, the load condition of service node and the number of user connections, we propose a load balancing strate...
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With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most approp...
With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.
Collision detection is a hot research topic in the field of path planning of mobile robot, virtual assembly simulation, and so on. Fast and accurate collision detection has become one of the most key technologies of r...
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Algorithms based on row enumeration always scan and construct conditional transposed tables, which increases the execution time and space cost. To address this problem, we adopt the DAG (Directed Acyclic Graph) to com...
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Algorithms based on row enumeration always scan and construct conditional transposed tables, which increases the execution time and space cost. To address this problem, we adopt the DAG (Directed Acyclic Graph) to compress the dataset to save the memory space. In DAG, each node is related to a rowid, and each two nodes have a corresponding directed edge which stores the common items of the two rowids. Each row is given an integer according to its coming order and the DAG follows that order. A directed acyclic graph records the relation between rows and items by doing AND(&) operation with the nodes' binary code of the edges. We also present DAGHDDM which is a new approach for mining frequent closed itemsets in high dimensional datasets. In this algorithm, we adopt the BitTable to compress the dataset firstly, and then construct DAG according to the BitTable. We increase the same items of the adjacent edges to implement pattern growth, traverse the DAG in reversal way and adopt a close-checking method to generate all frequent closed itemsets. It scans the dataset only once and does not generate candidate itemsets. The experimental results show that the proposed DAGHDDM algorithm can decrease the cost of time.
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