Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemsets mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation min...

Frequent pattern discovery is a heavily focused area in data mining. Discovering concealed information from Web log data is called Web usage mining. Web usage mining discovers interesting and frequent user access patterns from web logs. This paper contains a novel approach, based on k-mean and frequent pattern tree (FP-tree), for frequent pattern mining from Weblog data.

background : the concepts of ‘intelligence’ and ‘intellectual performance’ though seem alike, but are different. intelligence assessment of an individual is technical, is done by the application of multiple, reliable and validated, iq (intelligence quotient) tests on the same individual in different settings (date, place and time). however, ‘intellectual performance’ (ip) has a reference only t...

Mining Frequent Patterns in transaction database TD has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithm does not consider the time stamps associated with the transactions. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applicat...

An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree, which retains the itemset association information. It then divides the compressed database into a...

Share-frequent pattern mining discovers more useful and realistic knowledge from database compared to the traditional frequent pattern mining by considering the non-binary frequency values of items in transactions. Therefore, recently share-frequent pattern mining problem becomes a very important research issue in data mining and knowledge discovery. Existing algorithms of share-frequent patter...

Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining ca...

ion Huge amounts of moving object data have been collected with the advances in wireless communication and positioning technologies. Trajectory patterns extracted from historical trajectories of moving objects can reveal important knowledge about movement behavior for high quality LBS services, especially for location prediction. Existing approaches cannot forecast accurate locations in the dis...