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Algorithm PTPSPM (a parallel algorithm based on prefix tree for sequence pattern mining) is proposed in order to deal with the speed limited and effectiveness problem of the sequence pattern mining in massive data. In this paper, a new prefix-tree structure and an improved prefix-span algorithm are introduced to mine the local sequence, the global sequence are obtained by merging all the local sequences...
Frequent itemsets mining is a crucial problem in the field of data mining. Although many related studies have been suggested, these algorithms may suffer from high computation cost and spatial complexity in dense database, especially when mining long frequent itemsets or support threshold is very lower. To address this problem, a new data structure called P Array is proposed. P Array makes use of...
In the real manufacturing process, many conditions influence its process. However, there are three important factors consisting of final customer feedback information, flexible manufacturing and supplier selection affect the manufacturing process. In order to satisfy the real requirements of manufacturing process, this paper employs association rule mining of data mining technology including the above...
In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with time delays is analyzed. The delays are time-invariant, and the uncertainties are norm bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic...
For the ecological environment and bio-diversity protection at Jiansanjiang area, China, thesis adopted multi-scale remote sensing image segmentation method to detect an ecological corridor between two national nature reserves Sanjiang(SJNR) and Honghe(HHNR). The width of this corridor was detected by calculating the division threshold value of NDVI standardized change intensity index. And the structure...
This work proposes an on-the-fly testing approach for Web applications based on automaton theory. Web applications and test directives which are used to select test cases are modeled by FSM (Finite State Machine). Synchronous product is used to combine these two kinds of FSMs in order to check whether the SUT (System Under Test) includes the behavior expressed by the test directive. Test cases are...
A testing approach based on FSM and UML for Web applications is proposed in this paper in order to generate test cases automatically. Web applications are modeled using FSM and the test purpose which is a partial behavior of the SUT (system under testing) are specified by UML sequence diagrams, which can be converted automatically into FSM. Synchronous product is used to combine these two kinds of...
This paper proposes an automatic Web service composition method which considers both services input-output type compatibility and behavioral constraint compatibility based on Petri nets to raise the composition reliability. It presents a novel technique for discovering semantic relations between pre and post conditions of different services using their ontological descriptions. The technique takes...
The quality delivered by existing Web applications is often poor. In this paper, the formal verification techniques for web compositions are presents, and the formal models of web composition are proposed. The definition of well-typedness and congruence of web composition basing on pi-calculus are given. At the same time, this paper analysis both static and dynamic composition, replacement of web...
The problem of time series classification has drawn intensive attention from the data mining community. Conventional time series model may be unsuitable for multivariate motion time series because of the large volume of the data, highly correlated dimensions and rapid growth nature. In this paper, we propose C3M, an effective classification model for motion time series classification, which consists...
A good concept drifting stream classifier should have the following two characteristics: 1) sensitive to the new concept when concept drifts; 2) have stable high accuracy when concept is stable. Most published methods and algorithms may succeed in one aspect while neglecting the other. In this paper, we proposed an adaptive ensemble classifier for concept drifting stream classification which focuses...
Digital audio watermarking is embedded inaudible information into digital audio data for the purposes of copyright protection, ownership verification, covert communication, and/or auxiliary data carrying. In this paper, a new digital audio watermarking algorithm is proposed, which based on Neural Networks and in the Wavelet Domain and corresponding algorithms of watermark generation, embedding, and...
In the real world concepts are not stable and change with time and a lot of other hidden factors. Stream classifiers should be sensitive to the drifting of concept in an automatic way. In this paper, we proposed a new weighted majority strategy for the ensemble classifier. We periodically created and evaluated component classifiers that constitute the ensemble then we used the weighted ensemble to...
Frequent patterns mining has been studied popularly in KDD research. However, little work has been done on incremental updating frequent patterns mining. In a real transaction database, as time changing many new data may be inserted into previous database. But it is hard to handle incremental updating problems with FP-growth algorithm. In this paper, a novel incremental updating pattern tree (INUP_Tree)...
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