| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 93 === Hardware enhanced mining is an emerging issue. In this thesis, we propose two frameworks to enhance the speed of mining problems: temporal pattern mining in data streams and K-means clustering algorithm. By exploiting the parallelism in hardware, many data mining primitive subtasks can be executed with high throughput, thus increasing the performance of the overall data mining tasks. Specifically, in temporal pattern mining we realize Apriori-like algorithm within our proposed hardware enhanced mining framework. Even with the quadratic increase of the size of 2-itemsets, the counts of frequent 1-itemsets and 2-itemsets are obtained after one pass of the datasets through our hardware implementation, thus the throughput is maintained at constant level. Moreover, we propose a KACU (standing for K-means with hArdware Centroid updating) framework which integrates a hardware centroid updating mechanism into the procedure of continuous K-means algorithm. The proposed hardware frameworks are implemented in commercial Field Programmable Gate Array (FPGA) devices in order to measure their performance. The experimental results show that the hardware enhancements achieve considerably higher performance than traditional mining algorithm architectures with pure software implementation.
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