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The former is an Apriori-like algorithm that can mine inter-sequence patterns, but it is not efficient.
We also present a text mining framework to mine inter-sentential language patterns by associating frequently co-occurring language patterns across the sentence boundary.
Therefore, this study develops a text mining framework by extending the classical association rule mining algorithm [ 24- 28] such that it can mine inter-sentential language patterns by associating frequently co-occurred patterns across the sentence boundary.
Mine, mine, mine…".
The latter, a new method that we propose, employs several mechanisms for mining inter-sequence patterns efficiently.
The algorithm is based on mining inter-transaction association, and is mainly characterized by the use of a single tree-based data structure for generation and storage of all frequent sequences discovered by mining.
3D modeling first needs to establish pairwise epipolar geometry relationships within photo collections, thus provides a good example for mining inter-element connections within unordered visual datasets.
In this paper, we propose an algorithm called PITP-Miner that utilizes a projection based approach to mine frequent inter-transaction patterns efficiently.
As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools, inter alia.
We have observed mining closed inter-sequence patterns instead of frequent ones can lead to a more compact yet complete result set.
The problem of mining multidimensional inter-transactional association rules was recently introduced in [ACM Trans. Inform. Syst. 18(4) (2000) 423; Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, p. 12 1].
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