Periodic Pattern Mining a Algorithms and Applications

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network

GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter

Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals

[[abstract]]Many applications of sequential patterns require a guarantee of a particular event happening within a period of time. We propose CAI-PrefixSpan, a new data mining algorithm to obtain confident timed sequential patterns from sequential databases. Based on PrefixSpan, it takes advantage of the pattern-growth approach. After a particular event sequence, it would first calculate the confidence level regarding the eventual occurrence of a particular event. For those pass the minimal confidence requirement, it then computes the minimal time interval that satisfies the support requirement. It then generates corresponding projected databases, and applies itself recursively on the projected databases. With the timing information, it obtains fewer but more confident sequential patterns. CAI-PrefixSpan is implemented along with PrefixSpan. They are compared in terms of numbers of patterns obtained and execution efficiency. Our effectiveness and performance study shows that CAI-PrefixSpan is a valuable and efficient approach in obtaining timed sequential patterns.[[sponsorship]]IEEE[[conferencetype]]國際[[conferencedate]]20140813~20140815[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, U.S.A