@inproceedings{79bb7a7bb7164c0187506576026ce95f,
title = "Enhancing SWF for incremental association mining by itemset maintenance",
abstract = "Incremental association mining refers to the maintenance and utilization of the knowledge discovered in the previous mining operations for later association mining. Sliding window filtering (SWF) is a technique proposed to filter false candidate 2-itemsets by segmenting a transaction database into partitions. In this paper, we extend SWF by incorporating previously discovered information and propose two algorithms to boost the performance for incremental mining. The first algorithm FI-SWF (SWF with Frequent Itemset) reuses the frequent itemsets of previous mining task to reduce the number of new candidate itemsets that have to be checked. The second algorithm CI SWF (SWF with Candidate Itemset) reuses the candidate itemsets from the previous mining task. Experiments show that the new proposed algorithms are significantly faster than SWF.",
author = "Chang, {Chia Hui} and Yang, {Shi Hsan}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.; 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 ; Conference date: 30-04-2003 Through 02-05-2003",
year = "2003",
doi = "10.1007/3-540-36175-8_30",
language = "???core.languages.en_GB???",
series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",
publisher = "Springer Verlag",
pages = "301--312",
editor = "Kyu-Young Wang and Jongwoo Jeon and Kyuseok Shim and Jaideep Srivastava",
booktitle = "Advances in Knowledge Discovery and Data Mining",
}