Web Usage Mining is a research area that manipulates users’ click stream data in order to identify interesting traversal patterns of visitors accessing the website. As the clickstream data is expanding over the time; the process of discovering users’ frequent sequential pattern becomes a challenge due to the very low support distribution among the itemsets. By applying a Frequent Pattern Discov...

In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of frequent items over a sliding time window with sufficient accuracy. Prior to our work, the best solution is given by Cormode et al. [1], who gave an ...

Sequential pattern mining is a challenging task in data mining area with large applications. One among those applications is mining patterns from weblog. Recent times, weblog is highly dynamic and some of them may become absolute over time. In addition, users may frequently change the threshold value during the data mining process until acquiring required output or mining interesting rules. Som...

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance ...

In recent years, there are a great deal of efforts on sequential pattern mining, but some challenges have not been resolved, such as large search spaces and the ineffectiveness in handling highly similar, dense and long sequences. This paper mainly focuses on how to design some effective search space pruning methods to accelerate the mining process. We present a novel structure, PrefixFrequent-...

The problem of detecting frequent items in streaming data is relevant to many different applications across many domains. Several algorithms, diverse in nature, have been proposed in the literature for the solution of the above problem. In this paper, we review these algorithms, and we present the results of the first extensive comparative experimental study of the most prominent algorithms in ...

We investigate the problem of finding the frequent items in a continuous data stream. We present an algorithm called λ-Count for computing frequency counts over a user specified threshold on a data stream. To emphasize the importance of the more recent data items, a fading factor  is used. Our algorithm can detect εapproximate frequent items of a data stream using O(logλε) memory space and O(1...

Frequent Itemset mining extracts novel and useful knowledge from large repositories of data and this knowledge is useful for effective analysis and decision making in telecommunication networks, marketing, medical analysis, website linkages, financial transactions, advertising and other applications. The misuse of these techniques may lead to disclosure of sensitive information. Motivated by th...

Association Rule Mining (ARM) finds the interesting relationship between presences of various items in a given database. Apriori is the traditional algorithm for learning association rules. However, it is affected by number of database scan and higher generation of candidate itemsets. Each level of candidate itemsets requires separate memory locations. Hash Based Frequent Itemsets Quadratic Pro...

Mining association rules with multiple minimum supports is an important generalization of the association-rule-mining problem, which was recently proposed by Liu et al. Instead of setting a single minimum support threshold for all items, they allow users to specify multiple minimum supports to reflect the natures of the items, and an Apriori-based algorithm, named MSapriori, is developed to min...