An Intelligent Query Assistance for a Multidimensional Association Mining System

• Main Authors: Chang-Long Jiang, 江長隆
• Other Authors: Wen-Yang Lin
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/98430626713161437912

碩士 === 國立高雄大學 === 電機工程學系碩士班 === 98 === Association mining discovers interesting associations among items in large data sets. These association rules mined can be helpful to decision making processes in the business, scientific, medical and many other fields. A multidimensional association mining allows exploration of associations of items in different attributes (dimensions). Users can specify more precisely the mining data via settings of interested mining attributes, data granularity and optional filtering conditions. Thus it is vigorous that the rules mined tend to be closer to what the users want. Yet, it is a challenge for an inexperienced user to formulate a correct and effective query, especially on the settings of reasonable thresholds. Apriori algorithm for mining association rule requires the users to specify a minimum support to determine if an itemset is frequent or not. The minimum support is an influential factor to the mining results. Unfortunately, the setting of support threshold is subjective without clear standard. The users usually do try-and-error repeatedly until the mining process converges to satisfactory results. In this thesis we have two research focuses. First, we implement an user interface to realize the intelligence assistance functions embedded in the OntoWM system, a system framework of multidimensional association mining incorporating the ontologies. Second, we develop a mechanism for generating a minimum support that is suitable for the user’s mining intension and suggest it to the user. The method utilizes the query log of the mining history which is the case-base reasoning like resource. The system finds from the query log the K-nearest neighbors of similar queries to the user’s mining intension, aggregates them and obtains the favorable support range for the user to refer. We also provide experiments with statistic data drawing from mining process with and without intelligent assistance in query formulation. The result shows that with intelligent assistance, the mining process is more efficient.