AIM: Another Itemset Miner
نویسندگان
چکیده
We present a new algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the efficiency of the mining task. We integrate a combination of these techniques into an algorithm which utilize those techniques dynamically according to the input dataset. The algorithm main features include depth first search with vertical compressed database, diffset, parent equivalence pruning, dynamic reordering and projection. Experimental testing suggests that our algorithm and implementation significantly outperform existing algorithms/implementations.
منابع مشابه
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets
For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. A frequent itemset P is maximal if P is included in no other frequent itemset, and closed if P is included in no other itemset included in the exactly same transactions as P . The problems of finding these frequent itemsets are fundamental in data mining, and from the applicatio...
متن کاملFast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support
Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting res...
متن کاملFHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning
High utility itemset mining is a challenging task in frequent pattern mining, which has wide applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation and performs a depth-first search to discover patterns and calculate their utility without performing costly database scans. Although, this approach is effective, mining high-utility itemsets remains computati...
متن کاملAn efficient algorithm to mine high average-utility itemsets
With the ever increasing number of applications of data mining, high-utility itemset mining (HUIM) has become a critical issue in recent decades. In traditional HUIM, the utility of an itemset is defined as the sum of the utilities of its items, in transactions where it appears. An important problem with this definition is that it does not take itemset length into account. Because the utility o...
متن کاملEFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining
High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to mo...
متن کامل