Papers by Author: Ling Chen

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Authors: Wei Liu, Ling Chen
Abstract: In order to overcome the shortcomings of traditional algorithms, the algorithm MSPM was proposed. It used longer patterns for mining, which avoided producing lots of patterns with short length. Meanwhile by the use of prefix tree of primary frequent patterns, we extended the primary patterns which avoided plenty of irrelevant patterns. The experimental results show that MSPM not only improves the performance but also achieves effective mining results.
Authors: Ke Ming Tang, Cai Yan Dai, Ling Chen
Abstract: Mining closed frequent itemsets in data streams is an important task in stream data mining. Most of the traditional algorithms for mining closed frequent itemsets are Apriori-based which find the frequent itemsets from large amount of candidates, and needs a great deal of time and space. In this paper, an algorithm ItemListFCI for mining closed frequent itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a ItemList where the transactions and itemsets are recorded by the column and row vectors respectively. The algorithm first builds the ItemList for the first sliding window. Frequent closed itemsets can be detected by pair-test operations on the binary numbers in the Table. After building the first ItemList, the algorithm updates the ItemList for each sliding window. The frequent closed itemsets in the sliding window can be identified from the ItemList. Algorithms are also proposed to modify ItemList when adding and deleting a transaction. The experimental results on synthetic and real data sets indicate that the proposed algorithm needs less CPU time and memory than other similar methods.
Authors: Chao Wang, Hong Bin Zhang, Jing Guo, Ling Chen
Abstract: Job shop scheduling is a key technology in modern manufacturing. Scheduling performance will decide the enterprises’ core competitiveness. In this paper, improved reinforcement learning with cohesion is used in dynamic job shop environment, and it eased the contradiction of precocious and slow convergence. Also the machine choice is considered. So the dual scheduling which included job and machine is achieved in this system. And it obtains better results through the experiments. The utilization of equipments and the emergency handling capacity can be improved in the dynamic environment.
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