The single minimum support (minsup) based frequent pattern mining approaches like Apriori and FP-growth suffer from“rare item problem”while extracting frequent patterns. That...
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous...
In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, w...
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we pr...
A major challenge in frequent-pattern mining is the sheer size of its mining results. In many cases, a high min sup threshold may discover only commonsense patterns but a low one ...
Abstract. The paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially inte...
Association rule mining in real-time is of increasing thrust in many business applications. Applications such as e-commerce, recommender systems, supply-chain management and group...
When computationally feasible, mining extremely large databases produces tremendously large numbers of frequent patterns. In many cases, it is impractical to mine those datasets d...
One of the major problems in frequent pattern mining is the explosion of the number of results, making it difficult to identify the interesting frequent patterns. In a recent pap...
Finding frequent patterns from databases has been the most time consuming process in data mining tasks, like association rule mining. Frequent pattern mining in real-time is of in...