| Summary: | 博士 === 元智大學 === 資訊管理學系 === 101 === As computer technology progresses rapidly, its capacity to store and manage data in database has become crucial.
Though computer technology development facilitates data processing and eases demands on storage media, extraction of available implicit information to aid decision making has turn into a new and challenging task.
However, data mining is the exploration and analysis of data in order to discover meaningful patterns.
It can effectively be applied on all varieties of analysis and assist the process of decision-making in businesses.
In data mining, finding association rules in transaction database is most commonly seen.
The purpose is to search for the relation that exists among items of database.
The relation reflects that items ($X$) appear, other items ($Y$) are likely to appear as well.
For instance, when a customer purchases bread, one might also get milk along with it.
Accordingly, association rules can assist decision makers to scoop out the possible items that are likely to be purchased by consumers in the hopes to facilitate marketing strategies.
In this dissertation, we combine FP-growth with the concept of fuzzy sets to mine fuzzy association rules.
Using fuzzy sets means that we consider fuzzy knowledge representations described by the natural language are well suited for the subjective thinking of human subjects and will assist users in making decisions flexibly.
Therefore, the fuzzy partition method can be comprehensible by human users.
Next, because Apriori algorithm is not efficient in handing drawbacks for huge data and its time complexity with greater information and rapid growth, the technology of association rule was selected by FP-growth algorithm.
The main aim of this dissertation is to develop novel fuzzy data mining techniques for quantitative data to find comprehensible and hidden useful fuzzy knowledge based on the fuzzy partition method and FP-growth to solve various decision problems.
In the first algorithm, the fuzzy association rules are extracted from transaction database.
In this algorithm, we use fuzzy partition method to transform quantitative data into fuzzy knowledge and apply FP-growth algorithm to mine.
In the second algorithm, we increase the table structure in FP-growth algorithm, thus it is more efficient than the first method.
In addition, the parameters needed in the mining process and the importance of items evaluated by managers are given as linguistic terms, which are more natural and understandable for human beings.
Hence, in the third method, we proposed fuzzy weighted association rule to mine. Finally, we propose three different mining methods to compare with other methods, and the experiment results were verified that the methods proposed have great efficiency.
Besides, the proposed approaches are superior to Apriori algorithm. Then the second approach also has more efficiency than the first one.
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