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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/49304

Title: 使用結束標準來挖掘有時間限制之頻繁序列式樣
Mining Frequent Closed Sequential Patterns with Time Constraints Using Termination Criteria
Authors: Thi-Van Nguyen
Contributors: NTOU:Department of Computer Science and Engineering
Keywords: 資料挖掘;封閉樣板;時間限制;挖掘頻繁樣板
Data mining;closed patterns;time constraints;mining frequent patterns
Date: 2015
Issue Date: 2018-08-22T06:56:28Z
Abstract: 於本篇論文中,一產生有時間限制的封閉頻繁序列模式之方法被發展出來。 本論文所提之方法利用提升結束之準則來加速模式之產生。本方法利用前進跟後退之方法來發現頻繁封閉模式。從實驗結果中,我們發現使用T10I4D100K所產生之序列資料庫(序列資料數=5000,最小差距=5,最大差距=16,移動視窗=3,最小支持閥值=0.5%),本論文所提之方法可減少CTSP的計算時間約51.2%。
In this thesis, a frequent closed sequential patterns with time constraints generation algorithm is developed. The proposed method uses the developed early termination criteria to speed up the process of generating patterns. By determining a set of valid items, which are used for generating type-1 or type-2 patterns, this method performs forward and backward checking to find frequent closed patterns. From the experimental results, compared with CTSP, we can find the presented approach can reduce the computing time by 51.2% using the sequential databases, generated from T10I4D100k data set, with number of data sequences = 5000, minimum gap = 5, maximum gap = 16, sliding window = 3 and minimum support threshold = 0.5%. Using the same data set, the proposed method can reduce the computing time of CTSP by 38.54% with number of data sequences = 5000, minimum gap = 5, maximum gap = 16, sliding window = 3 and minimum support threshold = 1%. These results show that this method is more remarkable when a larger data set with smaller minimum support threshold is used.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010257056.id
Appears in Collections:[資訊工程學系] 博碩士論文

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