Cheng-Ru Lin

Learn More
We explore in this paper an effective sliding-window filtering (abbreviatedly as SWF) algorithm for incremental mining of association rules. In essence, by partitioning a transaction database into several partitions, algorithm SWF employs a filtering threshold in each partition to deal with the candidate itemset generation. Under SWF, the cumulative(More)
In this paper, we explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication(More)
Data clustering has attracted a lot of research attention in the field of computational statistics and data mining. In most related studies, the dissimilarity between two clusters is defined as the distance between their centroids, or the distance between two closest (or farthest) data points. However, all of these measurements are vulnerable to outliers,(More)
In this paper, we propose the techniques of slice scan and selective hash for episode mining. Mining episodes means the discovery of frequent episodes among events that occur relatively close to each other in a time series. Intuitively, this process could be done iteratively in the sense that the frequent episodes discovered in one iteration will be used as(More)