¹Dept. of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan
²Dept. of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan

---

Abstract—In real world applications, most transaction databases are often large and constantly updated. Current data mining algorithms face the problem of processing a large number of transactions in dynamic environments. Since memory space is limited, it is critical to be able to use available storage efficiently and to process more transactions. In this paper, we propose an improved data structure of a compressed FP-tree to mine frequent itemsets with greater efficiency. Use of our method can minimize the I/O overhead, and, more importantly, it can also perform incremental mining without rescanning the original database. Our experimental results show that the method we propose not only requires less memory, but also performs incremental mining more efficiently.

Index Terms—Association rule, Frequent pattern, Compressed FP-tree, Incremental data mining

[PDF]

Cite: Chien-Min Lin, Yu-Lung Hsieh, Kuo-Cheng Yin, Ming-Chuan Hung, Don-Lin Yang, "ADMiner: An Incremental Data Mining Approach Using a Compressed FP-tree," Journal of Software vol. 8, no. 8, pp. 2095-2103, 2013.