Evolution patterns and gradual trends

Abstract

Nowadays, many databases record ordered or temporally annotated data, such as Web access logs or genomic sequences. Therefore, sequence mining has become an important research area. Among these data mining approaches, sequential patterns aim at describing frequent behaviors. In the access data of a commercial Web site, one may, for instance, discover that "35% of customers successively buy a PSP then a memory stick and PSP games". To provide more complete information, fuzzy sequential patterns were designed, including quantitative values within the mining task. Such patterns, considering the previous example, would be "35% of customers buy a PSP, then they buy few games many times, and then they buy a high-capacity memory stick once." However, symbolic or fuzzy sequential patterns, in their current form, do not allow to extract temporal tendencies that are typical of sequential data. By means of temporal tendency mining, one may discover in the same access data that "An increasing number of purchases of PSP games during a very short period is frequently followed by a purchase of a high-capacity memory stick a few days later." It would be easy to conclude that the users either quickly succeed in registering or make several attempts before they look at the help page within a few seconds. To the best of our knowledge, no method has been designed for discovering this kind of patterns. Therefore, we propose, in this paper, two approaches that extract pattern-expressing trends or evolution. First, we define evolution patterns that summarize the evolution of the quantities in the data. We explain how they can be obtained from a quantitative sequence database. Second, we define gradual trends in fuzzy sequential data. These trends describe variations in the fulfillment of fuzzy properties according to time. For both kinds of patterns, we developed algorithms that were implemented and tested on real data. C 2009 Wiley Periodicals, Inc.

Mining Fuzzy-Temporal Gradual Patterns

“…The latter area, discovering causal relationships can easily be conceptualized by a gradual pattern that correlates the causal effect among gradual items. As an illustration, [12] proposed algorithms that could mine evolution patterns and gradual trends such as: An increasing number of purchases of jam during a short period is frequently followed by a purchase of bream a few days later. The overall principle entailed converting a quantitative database into a variation database, which was converted into a membership degree database (also known as the trend database) which is mined for evolution patterns.…”

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

“…The sequential patterns mining problem was introduced by [13] in the context of the basket market problem and applied with success in many fields such as biology [29], Web mining [30,31] or the consumer marketing [32].…”

GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets

“…A second, more recent, category directly considers the numerical values of the attributes and applies to attribute covariation on the whole attribute universe [6,7]. In some works, the focus has also been put on describing the extent to which the degree increases between objects, especially in the framework of temporal digital data [8,9].…”