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Biological sequential frequent pattern mining is one of the important research fields in biological sequential data mining. In order to overcome the shortcomings of traditional algorithms, we proposed a fast algorithm SSPM here. We used longer patterns and prefix tree of primary frequent patterns for mining which avoided plenty of irrelevant patterns. The experimental results show that our algorithm...
Traditional Mining Frequent Pattems algorithms will construct lots of projected databases and generate lots of patterns with short length in the process of mining which cause the low efficiency of mining. In order to overcome the shortcomings of traditional algorithms, a fast and efficient algorithm SSPM was proposed. We used longer pattems for mining, which avoided producing lots of patterns with...
Water quality maintenance is always a focus in a fishpond in order to achieve a high harvest and good scenery. A wetland fishpond in Yingdong Village on Chongming Island, Shanghai has been studied. Through a 2D modeling analysis it is found that the water diversion from the neighboring Yangtze River is feasible and effective. The diversion can be conducted once every 2 months to achieve the desirable...
DNA gene sequences are the vectors of biologic genetic information. Z curves of gene sequences can convert character sequences into visual spatial curve, and it can improve the efficiency and accuracy of judging genetic sequences. This paper uses the geometrical center distance matrix of Z curve to analyze and compare gene sequences, and analyzes the effects of primary geometric center and secondary...
Biclustering the gene expressing data is an important task in bioinformatics. A parallel biclustering algorithm for gene expressing data is presented. The algorithm starts from the data sets containing pair of rows and columns of the data matrix, and gets the biclusters by gradually adding columns and rows on the data sets. A pruning technique is also proposed to reduce computing time. Experimental...
A biclustering algorithm for gene expressing data is presented. Based on the anti-monotones property of the quality of the data sets with their sizes, the algorithm can get the final biclusters by gradually adding columns and rows on the data sets. Experimental results show that our algorithm has higher processing speed and quality of clustering than other similar algorithms.
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