有效權重資料探勘方法之研究 = A Study on Efficient...
國立高雄大學資訊工程學系碩士班

 

  • 有效權重資料探勘方法之研究 = A Study on Efficient Approaches for Weighted Data Mining
  • 紀錄類型: 書目-語言資料,印刷品 : 單行本
    並列題名: A Study on Efficient Approaches for Weighted Data Mining
    作者: 李弘裕,
    其他團體作者: 國立高雄大學
    出版地: [高雄市]
    出版者: 撰者;
    出版年: 2012[民101]
    面頁冊數: 115面圖,表格 : 30公分;
    標題: 資料探勘
    標題: Data mining association-rule mining
    附註: 104年10月31日公開
    附註: 參考書目:面96-102
    摘要註: 於資料探勘的領域內,權重資料探勘因其實用性,在最近幾年被廣泛的討論。與過去的關聯規則探勘方法不同,權重探勘給予項目適當的權重以表示此項目在資料集合的重要性,這樣出現次數低但重要性高的項目集合可以被找出。只是,關聯規則探勘裡的向下封閉性在權重資料探勘裡不會成立。雖然過去使用的高估模型可以產生向下封閉性,可是在探勘過程,很多不需要產生的候選項目仍然會被產生。為了解決這個問題,我們設計了幾個有效率權重項目探勘方法與權重序列探勘方法。在權重項目探勘的議題中,我們提出採用一筆交易資料裡面最高權重當做高估值的新高估模型以得到比之前更準確的高估值。另外,兩個有效率的機制,修剪與過濾,被設計出去更進一步改善高估模型。為了有效率的使用新的高估模型與機制,我們先提出了投影基底演算法結合改進的高估模型,然後,另外一個投影基底演算法結合改進高估模型與有效的改進機制被提出並應用在找出權重項目。另一方面,在權重項目集探勘所提的概念可以被進一步的擴展到權重序列探勘。最後,實驗結果顯示出我們所提演算法之效能於不同的資料參數設定下優於之前的權重探勘演算法。 Weighted data mining in the field of data mining has been widely discussed in recent years due to its various practical applications. Different from traditional association-rule mining, an item on weighted data mining is flexibly given a suitable weight value to represent its importance in a database, and then weighted frequent itemsets can be found from a database. But, the downward-closure property in association-rule mining can be not kept in the weighted data mining. Although traditional upper-bound model can be applied to achieve the goal, lots of unpromising candidate itemsets still have to be generated by using the traditional model. To address this, we thus develop several efficient methods for mining weighted frequent itemsets and weighted sequential patterns.For the issue of weighted itemset mining, a new upper-bound model, which adopts the maximum weight in a transaction as upper-bound of the transaction, is first proposed to obtain more accurate upper-bound for itemsets. In addition, two effective strategies, pruning and filtering, are designed to further improve the model. To effectively utilize the model and strategies, the two efficient algorithms, projection-based weighted mining algorithms based on the improved upper-bound approach with the pruning strategy and projection-based weighted mining algorithms based on the improved upper-bound approach with effective strategies, are proposed for finding weighted frequent itemsets in databases. On the other hand, the proposed concepts on weighted itemset mining can be further extended to the problem of weighted sequential pattern mining. Finally, the experimental results on the synthetic and real datasets also show the performance of the proposed algorithms outperforms the traditional weighted mining algorithms under various parameter settings.
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