Mining Erasable Itemsets Research Articles

Damped weighted erasable itemset mining with time sensitive dynamic environments

Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing studies on weighted erasable itemset mining do not regard the time-sensitivity of data arrival times. In this paper, we propose a new weighted erasable itemset mining approach considering time-sensitive dynamic environments. For industrial manufacturers with automated control systems, our method focuses on recent data and item weights to effectively filter out unprofitable itemsets. Performance tests show that the proposed method outperforms state-of-the-art studies in runtime with on-par or minimal compromise in memory usage and that it scales capably with varying database sizes. We performed statistical analyses to demonstrate the correctness and significance of the discovered results from our proposed method. Furthermore, extended evaluations on sensitivity and resultant itemsets show how the algorithm responds to varying parameters as well as provide insights on the discovered itemsets.

Sử dụng hai ngưỡng khai thác tập có thể xóa trên dữ liệu tăng cường

Traditional data mining is often applied in static databases and bath processing. In fact, databases are often changed, bath processing is not suitable because it consumes more time to mine in the whole database. Therefore, mining in dynamic databases attracted many researchers where mining erasable itemsets (EIs) in incremental databases is one of interesting areas. Recent years, there are some publications developed for updated EIs in dynamic databases but they consume more time to rescan the original database. In this paper we propose an algorithm for updating EIs using two minimum thresholds to avoid rescanning databases, we also use new data structure to efficient process incremental databases.

Applicable Metamorphic Testing for Erasable-Itemset Mining

Erasable-itemset mining is often used in production planning to identify itemsets that, if removed, would make little effect on the production profits. Consequently, much recent attention has been focused on increasing the mining efficiency. Yet, in addition to mining efficiency, guaranteeing mining correctness is also an important issue. In real applications, wrong mining results might lead a company to make inappropriate decisions. Therefore, in this paper, we use the metamorphic testing, a lightweight software-testing strategy, to check the mining results via discovering proper metamorphic relations. The core idea contains five metamorphic relations oriented from two aspects. In the first aspect, we alter maximum thresholds without changing the input data, and in the second aspect, we modify product databases and material items with fixing maximum thresholds. In the experiments, 11 datasets and 76 mutants generated by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Java were used to discuss the effects of database parameters, including the average number of materials in products, the number of different material items, the number of products, and the maximum threshold, respectively. The experimental results show that the metamorphic relations deliver good assessment performances in terms of effectiveness and efficiency, especially the fourth metamorphic relation.

Fast algorithms for mining maximal erasable patterns

Since the problem of mining erasable itemsets was identified in 2009, many algorithms have been proposed to improve mining time and/or memory usage. However, algorithms for mining maximal erasable itemsets (MaxEIs) have not been developed, and this article therefore focuses on this problem. Firstly, a GenMax-based algorithm (GenMax-EI) is developed as a baseline algorithm. Secondly, a proposition is developed for fast checking of whether or not an erasable itemset is maximal, and based on this proposition, we develop an algorithm entitled Flag-GenMax-EI for the fast mining of MEIs. Finally, a second proposition for the fast pruning of non-MaxEIs is also developed; based on this proposition, we propose an algorithm entitled PE-GenMax-EI for mining MaxEIs.

Efficient Algorithms for Mining Erasable Closed Patterns From Product Datasets

Finding knowledge from large data sets to use in intelligent systems becomes more and more important in the Internet era. Pattern mining, classification, text mining, and opinion mining are the topical issues. Among them, pattern mining is an important issue. The problem of mining erasable patterns (EPs) has been proposed as a variant of frequent pattern mining for optimizing the production plans of factories. Several algorithms have been proposed for effectively mining EPs. However, for large threshold values, many EPs are obtained, leading to large memory usage. Therefore, it is necessary to mine a condensed representation of EPs. This paper first defines erasable closed patterns (ECPs), which can represent the set of EPs without information loss. Then, a theorem for fast determining ECPs based on dPidset structure is proposed and proven. Next, two efficient algorithms [erasable closed pattern mining (ECPat) and dNC_Set based algorithm for erasable closed pattern mining (dNC-ECPM)] for mining ECPs based on this theorem are proposed. Experimental results show that ECPat is the best method for sparse data sets, while dNC-ECPM algorithm outperforms ECPat algorithm and a modified mining erasable itemsets algorithm in terms of the mining time and memory usage for all remaining data sets.

Mining erasable itemsets with subset and superset itemset constraints

Erasable itemset (EI) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining EIs was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage. In reality, users only need a small number of EIs which satisfy a particular condition. Having this observation in mind, in this study we develop an efficient algorithm for mining EIs with subset and superset itemset constraints (C0⊆X⊆C1). Firstly, based on the MEI (Mining Erasable Itemsets) algorithm, we present the MEIC (Mining Erasable Itemsets with subset and superset itemset Constraints) algorithm in which each EI is checked with regard to the constraints before being added to the results. Next, two propositions supporting quick pruning of nodes that do not satisfy the constraints are established. Based on these, we propose an efficient algorithm for mining EIs with subset and superset itemset constraints (called pMEIC – p: pruning). The experimental results show that pMEIC outperforms MEIC in terms of mining time and memory usage.

Erasable itemset mining over incremental databases with weight conditions

Erasable itemset mining is an approach for mining itemsets with low profits from large-scale product databases in order to solve financial crises of plants in manufacturing industries. Previous erasable itemset mining methods deal with static product databases only, and ignore any characteristics such as items’ own values when they extract the erasable itemsets. Therefore, such approaches may fail to solve financial crises of plants because they have to iterate a significant number of mining processes in order to deal with real-time product data accumulated from plants in the real world. In this paper, we propose a new tree-based erasable itemset mining algorithm for dynamic databases, which finds erasable itemsets considering the weight conditions from incremental databases. The proposed algorithm uses new tree and list data structures for performing its mining operations more efficiently. Furthermore, the proposed algorithm is capable of reducing the number of mined erasable itemsets by considering the different weight information of items within product databases. We also compare the proposed approach with other tree-based state-of-the-art methods. By performing runtime, memory, pattern quality, and scalability comparisons with respect to various real and synthetic incremental datasets, we show that the proposed algorithm is outstanding in comparison to other previous methods.

EIFDD: An efficient approach for erasable itemset mining of very dense datasets

Erasable itemset mining, first proposed in 2009, is an interesting problem in supply chain optimization. The dPidset structure, a very effective structure for mining erasable itemsets, was introduced in 2014. The dPidset structure outperforms previous structures such as PID_List and NC_Set. Algorithms based on dPidset can effectively mine erasable itemsets. However, for very dense datasets, the mining time and memory usage are large. Therefore, this paper proposes an effective approach that uses the subsume concept for mining erasable itemsets for very dense datasets. The subsume concept is used to help early determine the information of a large number of erasable itemsets without the usual computational cost. Then, the erasable itemsets for very dense datasets (EIFDD) algorithm, which uses the subsume concept and the dPidset structure for the erasable itemset mining of very dense datasets, is proposed. An illustrative example is given to demonstrate the proposed algorithm. Finally, an experiment is conducted to show the effectiveness of EIFDD.

A survey of erasable itemset mining algorithms

Pattern mining, one of the most important problems in data mining, involves finding existing patterns in data. This article provides a survey of the available literature on a variant of pattern mining, namely erasable itemset (EI) mining. EI mining was first presented in 2009 and META is the first algorithm to solve this problem. Since then, a number of algorithms, such as VME, MERIT, and dMERIT+, have been proposed for mining EI. MEI, proposed in 2014, is currently the best algorithm for mining EIs. In this study, the META, VME, MERIT, dMERIT+, and MEI algorithms are described and compared in terms of mining time and memory usage. WIREs Data Mining Knowl Discov 2014, 4:356–379. doi: 10.1002/widm.1137This article is categorized under: Algorithmic Development &gt; Association Rules

MEI: An efficient algorithm for mining erasable itemsets

Erasable itemset (EI) mining is an interesting variation of frequent itemset mining which allows managers to carefully consider their production plans to ensure the stability of the factory. Existing algorithms for EI mining require a lot of time and memory. This paper proposes an effective algorithm, called mining erasable itemsets (MEI), which uses the divide-and-conquer strategy and the difference pidset (dPidset) concept for mining EIs fully. Some theorems for efficiently computing itemset information to reduce mining time up and memory usage are also derived. Experimental results show that MEI outperforms existing approaches in terms of both the mining time and memory usage. Moreover, the proposed algorithm is capable of mining EIs with higher thresholds than those obtained using existing approaches.

Improving Efficiency of META Algorithm Using Record Reduction

Erasable Itemset Mining is the key approach of data mining in production planning. The erasable itemset mining is the process of finding erasable itemsets that satisfy the constraint i.e. user defined threshold. Efficient algorithm to mine erasable itemsets is extremely important in data mining. Since the META Algorithm was proposed to generate the erasable itemsets. In last few years there have been several methods to improve its performance. But they do not consider the time constraint. If database is large META takes too much time to scan the database. In this paper, Author purposed an Improved META (I-META) algorithm which reduces the scanning time by reduction of production records. It also reduces the redundant generation of sub-items during trimming the candidate itemsets, which can find directly the set of erasable itemsets and removing candidate having a subset that is not erasable.

Mining Top-Rank-kErasable Itemsets by PID_lists

Mining erasable itemsets are one of new emerging data mining tasks. In this paper, we present a new data representation called a PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. On the basis of the PID_list, we propose a new algorithm called VM for mining top-rank-k erasable itemsets efficiently. The VM algorithm can avoid the time-consuming process of calculating the gain of the candidate itemsets and lots of scans of the databases. Therefore, it can accelerate the task of mining greatly. For evaluating the VM algorithm, we have conducted experiments on six synthetic product databases. Our performance study shows that the VM algorithm is efficient and much faster than the MIKE algorithm, which is the first algorithm for dealing with the problem of mining top-rank-k erasable itemsets.

Fast mining erasable itemsets using NC_sets

Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called NC_set, which keeps track of the complete information used for mining erasable itemsets. Based on NC_set, we propose a new algorithm called MERIT for mining erasable itemsets efficiently. The efficiency of MERIT is achieved with three techniques as follows. First, the NC_set is a compact structure, which prunes irrelevant data automatically. Second, the computation of the gain of an itemset is transformed into the combination of NC_sets, which can be completed in linear time complexity by an ingenious strategy. Third, MERIT can directly find erasable itemsets without generating candidate itemsets in some cases. For evaluating MERIT, we have conducted extensive experiments on a lot of synthetic product databases. Our performance study shows that the MERIT is efficient and is on average about two orders of magnitude faster than the META, the first algorithm for mining erasable itemsets.