Faculty of Science

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Browsing Faculty of Science by Author "Ahmed, Chowdhury Farhan"

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    An efficient approach for mining weighted frequent patterns with dynamic weights
    (ibai publishing, 2019-07) Dewan, Umama; Ahmed, Chowdhury Farhan; Leung, Carson K.; Rizvee, Redwan Ahmed; Deng, Deyu; Souza, Joglas
    Weighted frequent pattern (WFP) mining is considered to be more effective than traditional frequent pattern mining because of its consideration of different semantic significance (weights) of items. However, most existing WFP algorithms assume a static weight for each item, which may not be realistically hold in many real-life applications. In this paper, we consider the concept of a dynamic weight for each item and address the situations where the weights of an item can be changed dynamically. We propose a novel tree structure called compact pattern tree for dynamic weights (CPTDW) to mine frequent patterns from dynamic weighted item containing databases. The CPTDW-tree leads to the concept of dynamic tree restructuring to produce a frequency-descending tree structure at runtime. CPTDW also ensures that no non-candidate item can appear before candidate items in any branch of the tree, and thus speeds up the construction time for prefix tree and its conditional tree during the mining process. Furthermore, as it requires only one database scan, it can be applicable to interactive, incremental, and/or stream data mining. Evaluation results show that our proposed tree structure and the mining algorithm outperforms previous methods for dynamic weighted frequent pattern mining.
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    Open Access
    Sliding window based weighted periodic pattern mining over time series data
    (ibai publishing, 2019-07) Rizvee, Redwan Ahmed; Shahin, Md Shahadat Hossain; Ahmed, Chowdhury Farhan; Leung, Carson K.; Deng, Deyu; Mai, Jiaxing Jason
    Sliding windows have been crucial in mining time series. Many existing studies focus on reconstruction of the underlying structure (e.g., suffix tree) for each new window. However, when the window size is large or when the window slides frequently, reconstruction may perform poorly. In this paper, we propose a solution that dynamically updates the structure (rather than reconstruction for each modification or sliding). Moreover, many existing studies rely on the weight of maximum weighted item in the database to avoid testing unnecessary patterns when mining weighted periodic patterns from time series, but it may still require lots of weight checking to determine whether a pattern is a candidate. In this paper, we also propose an additional solution to address this problem by discarding unimportant patterns beforehand so as to speed up the candidate generation process. Evaluation results on real-life datasets show the effectiveness of our two solutions in handling sliding window and pruning redundant candidate patterns.
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    WeFreS: weighted frequent subgraph mining in a single large graph
    (ibai publishing, 2019-07) Ashraf, Nahian; Haque, Riddho Ridwanul; Islam, Md. Ashraful; Ahmed, Chowdhury Farhan; Leung, Carson K.; Mai, Jiaxing Jason; Wodi, Bryan H.
    Considering edge weights during frequent subgraph mining can help us discover more interesting and useful subgraph patterns when compared to its unweighted counterparts. Although some recent works have proposed weight adaptation in frequent subgraph mining from transactional graph databases, the consideration of edge-weights in mining subgraph patterns from single large graphs is mostly unexplored. However, such graph structures appear frequently, with instances being found in social networks, citation and collaboration graphs, chemical and biological networks, etc. In this paper, we propose WeFreS, an efficient algorithm for mining weighted frequent subgraphs in edge-weighted single large graphs. WeFreS takes into consideration the weight, or significance of the interactions between different types of entities, and only outputs subgraphs whose weighted support is greater than a given user-defined threshold. The resulting subgraph patterns are both frequent and significant from the application perspective. Moreover, for efficiency, WeFreS is also equipped with various pruning techniques and optimizations.