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Discover Sequential Patterns in Incremental Database
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17387
title: Discover Sequential Patterns in Incremental Database abstract: The task of sequential pattern mining is to discover the complete set of sequential patterns in a given sequence database with minimum support threshold. But in practice, minimum support some time is defined afterward, or need to be adjusted to discover information that interest to knowledge workers. In the same time, the problem of discover sequential patterns in a incremental database is an essential issue in real world practice of datamining. This paper discusses the issue of maintaining discovered sequential patterns when some information is appended to a sequence database. Many previous works based on Apriori-like approaches are not capable to do so without re-running previously presented algorithms on the whole updated database. We propose a novel algorithm, called DSPID, which takes full advantage of the information obtained from previous mining results to cut down the cost of finding new sequential patterns in an incremental database.
<br>An Algorithm for Mining Strong Negative Fuzzy Sequential Patterns
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17388
title: An Algorithm for Mining Strong Negative Fuzzy Sequential Patterns abstract: Many methods have been proposed for mining fuzzy sequential patterns. However, most of conventional methods only consider the occurrences of fuzzy itemsets in sequences. The fuzzy sequential patterns discovered by these methods are called as positive fuzzy sequential patterns. In practice, the absences of frequent fuzzy itemsets in sequences may imply significant information. We call a fuzzy sequential pattern as a negative fuzzy sequential pattern, if it also expresses the absences of fuzzy itemsets in a sequence. In this paper, we proposed a method for mining negative fuzzy sequential patterns, called NFSPM. In our method, the absences of fuzzy itemsets are also considered. Besides, only sequences with high degree of interestingness can be selected as negative fuzzy sequential patterns. An example was taken to illustrate the process of the algorithm NFSPM. The result showed that our algorithm could prune a lot of redundant candidates, and could extract meaningful fuzzy sequential patterns from a large number of frequent sequences.
<br>A Deflected Grid-based Algorithm for Clustering Analysis
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17392
title: A Deflected Grid-based Algorithm for Clustering Analysis abstract: The grid-based clustering algorithm, which partitions the data space into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure, is an efficient clustering algorithm, but its effect is seriously influenced by the size of the cells. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells, a new grid-based clustering algorithm, called DGD, is proposed in this paper. The main idea of DGD algorithm is to deflect the original grid structure in each dimension of the data space after the clusters generated from this original structure have been obtained. The deflected grid structure can be considered a dynamic adjustment of the size of the original cells, and thus, the clusters generated from this deflected grid structure can be used to revise the originally obtained clusters. The experimental results verify that, indeed, the effect of DGD algorithm is less influenced by the size of the cells than other grid-based ones.
<br>An Axis-Shifted Grid-Clustering Algorithm
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52779
title: An Axis-Shifted Grid-Clustering Algorithm abstract: These spatial clustering methods can be classified into four categories: partitioning method, hierarchical method, density-based method and grid-based method. The grid-based clustering algorithm, which partitions the data space into a finite number of cells to form a grid structure and then performs all clustering operations to group similar spatial objects into classes on this obtained grid structure, is an efficient clustering algorithm. To cluster efficiently and simultaneously, to reduce the influences of the size and borders of the cells, a new grid-based clustering algorithm, an Axis-Shifted Grid-Clustering algorithm (ASGC), is proposed in this paper. This new clustering method combines a novel density-grid based clustering with axis-shifted partitioning strategy to identify areas of high density in the input data space. The main idea is to shift the original grid structure in each dimension of the data space after the clusters generated from this original structure have been obtained. The shifted grid structure can be considered as a dynamic adjustment of the size of the original cells and reduce the weakness of borders of cells. And thus, the clusters generated from this shifted grid structure can be used to revise the originally obtained clusters. The experimental results verify that, indeed, the effect of this new algorithm is less influenced by the size of cells than other grid-based ones and requires at most a single scan through the data.
<br>Mining Strong Positive and Negative Sequential Patterns
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17391
title: Mining Strong Positive and Negative Sequential Patterns abstract: In data mining field, sequential pattern mining can be applied in divers applications such as basket analysis, web access patterns analysis, and quality control in manufactory engineering, etc. Many methods have been proposed for mining sequential patterns. However, conventional methods only consider the occurrences of itemsets in customer sequences. The sequential patterns discovered by these methods are called as positive sequential patterns, i.e., such sequential patterns only represent the occurrences of itemsets. In practice, the absence of a frequent itemset in a sequence may imply significant information. We call a sequential pattern as negative sequential pattern, which also represents the absence of itemsets in a sequence. The two major difficulties in mining sequential patterns, especially negative ones, are that there may be huge number of candidates generated, and most of them are meaningless. In this paper, we proposed a method for mining strong positive and negative sequential patterns, called PNSPM. In our method, the absences of itemsets are also considered. Besides, only sequences with high degree of interestingness will be selected as strong sequential patterns. An example was taken to illustrate the process of PNSPM. The result showed that PNSPM could prune a lot of redundant candidates, and could extract meaningful sequential patterns from a large number of frequent sequences.
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