改良型關聯規則探勘演算法之效能分析

由於進行多屬性且多紀錄之大型資料庫的資料探勘關聯分析執行的效能不彰，本研究為了有效找到符合之關聯規則，藉由整合Column-wise、Vector-based、Data Cutting and Sorting Method、Bitwise Operation等計算方式來減少整體計算量、加速搜尋高頻項目的集合，並使用目標屬性探勘為主，因此提出五種改良型Apriori演算法，針對兩個密集型及兩個稀疏型資料庫做效能測試，並同時和BitTableFI、Index-BitTableFI、Apriori等三種前人演算法進行效能比較。本論文的案例應用係採用內政部2008年新移民女性資料庫，首先進行了描述性統計和卡方檢定，繼之使用改良關聯規則探勘演算法進行相關參數值設定的不同(資料量、屬性量和支持度)以及記憶體使用量進行效能分析，目的就是為了快速找出人口屬性、社經條件與醫療健康和生活輔助需求之關聯性，最後並對應用案例的關聯規則結果進行相關探討。

關鍵字

資料探勘；關聯規則；改良型Apriori演算法

並列摘要

Many real world applications of association rule mining from multi-attribute and multi-record of large databases help users make better decisions. However, previous studies have produced big numbers of irrelevant patterns and much time is wasted for finding meaningful rules in large and sparse datasets. To efficiently discover interesting rules that connotes causality between antecedent and consequence in a target pattern, we propose five improved algorithms by integrating the Column-wise, Vector-based, Data Cutting and Sorting Method, and Bitwise Operation in this study. Experimental results show that the proposed algorithms can remove imprecise patterns, can fast discover target rules, and outperforms other three previous algorithms including Apriori, BitTableFI, and Index-BitTableFI, especially for lower supports in mining two intensive and two sparse databases. In addition, the proposed five improved algorithms were applied to find associations between socio-economic characteristics and life care needs for new immigrant women in Taiwan. The results also confirm that our approach is practical and effective with good performance for mining class-association rules in large databases.

並列關鍵字

Data Mining ； Association Rules ； Improved Apriori Algorithms

參考文獻

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國際替代計量

改良型關聯規則探勘演算法之效能分析

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