Items In Transaction Database Research Articles

Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.

Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to discover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based databases. It can be seen from a thorough performance evaluation that FEACP can identify useful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse databases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.

Dynamic Itemset Hiding Algorithm for Multiple Sensitive Support Thresholds

This article describes how association rule mining is used for extracting relations between items in transactional databases and is beneficial for decision-making. However, association rule mining can pose a threat to the privacy of the knowledge when the data is shared without hiding the confidential association rules of the data owner. One of the ways hiding an association rule from the database is to conceal the itemsets (co-occurring items) from which the sensitive association rules are generated. These sensitive itemsets are sanitized by the itemset hiding processes. Most of the existing solutions consider single support thresholds and assume that the databases are static, which is not true in real life. In this article, the authors propose a novel itemset hiding algorithm designed for the dynamic database environment and consider multiple itemset support thresholds. Performance comparisons of the algorithm is done with two dynamic algorithms on six different databases. Findings show that their dynamic algorithm is more efficient in terms of execution time and information loss and guarantees to hide all sensitive itemsets.

Mining Weighted Frequent Itemsets without Candidate Generation in Uncertain Databases

Frequent itemset mining (FIM) is a fundamental set of techniques used to discover useful and meaningful relationships between items in transaction databases. In recent decades, extensions of FIM such as weighted frequent itemset mining (WFIM) and frequent itemset mining in uncertain databases (UFIM) have been proposed. WFIM considers that items may have different weight/importance. It can thus discover itemsets that are more useful and meaningful by ignoring irrelevant itemsets with lower weights. UFIM takes into account that data collected in a real-life environment may often be inaccurate, imprecise, or incomplete. Recently, these two ideas have been combined in the HEWI-Uapriori algorithm. This latter considers both item weights and transaction uncertainty to mine the high expected weighted itemsets (HEWIs) using a two-phase Apriori-based approach. Although the upper-bound proposed in HEWI-Uapriori can reduce the size of the search space, it still generates a large amount of candidates and uses a level-wise search. In this paper, a more efficient algorithm named HEWI-Utree is developed to efficiently mine HEWIs without performing multiple database scans and without generating candidates. This algorithm relies on three novel structures named element (E)-table, weighted-probability (WP)-table and WP-tree to maintain the information required for identifying and pruning unpromising itemsets early. Experimental results show that the proposed algorithm is generally much more efficient than traditional methods for WFIM and UFIM, as well as the state-of-the-art HEWI-Uapriori algorithm, in terms of runtime, memory consumption, and scalability.

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Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental problems and most time-consuming in association rule mining. Most of the algorithms in literature used to find frequent itemsets satisfying single minimum support threshold. In practice, frequentcy of each item reflects the nature and role of items in transactional databases. This paper proposes an efficient mining parallel algorithm for frequent itemsets with multiple minimum support thresholds (a different minimum item support for each item) on Multiple-core Processors. Proposed algorithm easily extends on distributed computing systems as Hadoop, Spark. Finally, result experiments presented on both synthetic and real-life datasets show the better proposed algorithm than the existing algorithms.

Weighted frequent itemset mining over uncertain databases

Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from tw...

Discovery of Association Rules from University Admission System Data

Abstract —Association rules discovery is one of the vital data mining techniques. Currently there is an increasing interest in data mining and educational systems, making educational data mining (EDM) as a new growing research community. In this paper, we present a model for association rules discovery from King Abdulaziz University (KAU) admission system data. The main objective is to extract the rules and relations between admission system attributes for better analysis. The model utilizes an apriori algorithm for association rule mining. Detailed analysis and interpretation of the experimental results is presented with respect to admission office perspective. Index Terms —Educational Data Mining, Association Rules Discovery, University Admission System. I. I NTRODUCTION Data mining aims at the discovery of useful information from large collection of data. Recently, there are increasing research interests in using data mining in education. This newly emerging field, called Educational Data Mining (EDM), concerns with developing methods that discover knowledge from data originating from educational environments [1]. Data mining techniques can discover useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for decisions when designing or modifying an environment or teaching approach. The application of data mining in educational systems is an iterative cycle of hypothesis formation, testing, and refinement. As we can see in Fig. 1, educators and academics responsible are in charge of designing, planning, building and maintaining the educational systems. Different data mining techniques can be applied in order to discover useful knowledge that helps to improve both the academic and management processes [2]. Association rule mining is one of the major data mining techniques that interested in finding strong relationships and correlation among items in transactional databases. It can be employed in many areas including market analysis, decision support systems and financial forecast. An association rule has two measures: support and confidence that represent its statistical significance [3]. The problem of mining association rule is to discover the implication relation among items such that the presence of some items implies the presence of other items in the same transaction. Figure 1. Data Mining Cycle in Educational System [2]Mainly, all the proposed algorithms for association rules mining deals with transactional databases or market-basket data. These algorithms do not support relational databases naturally. To apply the same concepts and algorithms, relational database has to be converted to the transactional representation [4]. This requires the application of tedious conversion processes on large quantities of data before such algorithms can be applied as discussed with more details later in the paper.

An Optimal Approach to derive Disjunctive Positive and Negative Rules from Association Rule Mining using Genetic Algorithm

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. Association rule mining is one of the most important techniques of data mining that aims to induce associations among sets of items in transaction databases or other data repositories. There are various Algorithms developed and customized to derive the effective rules to improve the business. Amongst all, Apriori algorithms and FP Growth Algorithms play a vital role in finding out frequent item set and subsequently deriving rule sets based on business constraints. However there are few shortfalls in these conventional Algorithms. They are i) candidate items generation consumes lot of time in the case of large datasets ii) It supports majorly the conjunctive nature of association rules iii) The single minimum support factor not suffice to generate the effective rules iv) 'support/confident' alone not helping to validate the rules generated and v) Negative rules are not addressed effectively. Points from i) to iv) were addressed in the earlier works (10)(13) . However identifying and deriving negative rules are still a challenge. The proposed work is considered to be the extended version of our earlier work (13). It focuses how effectively negative rules can be derived with the help of logical rules sets which was not addressed in our earlier work. For this exercise the earlier work has been taken as the reference and the appropriate modifications and additions are updated into it where ever applicable. Hence by using this approach conjunctive & disjunctive; positive& negative rules can be generated effectively in an optimized manner.

Mining Method for Weighted Concise Association Rules Based on Closed Itemsets under Weighted Support Framework

Association rules tell us interesting relationships between different items in transaction database. But traditional association rule has two disadvantages. Firstly it assumes every two items have same significance in database, which is unreasonable in many real applications and usually leads to incorrect results. On the other hand, traditional association rule representation contains too much redundancy which makes it difficult to be mined and used. This paper addresses the problem of mining weighted concise association rules based on closed itemsets under weighted support-significant framework, in which each item with different significance is assigned different weight. Through exploiting specific technique, the proposed algorithm can mine all weighted concise association rules while duplicate weighted itemset search space is pruned. As illustrated in experiments, the proposed method leads to good results and achieves good performance.

Predicting Missing Items in Shopping Cart using Associative Classification Mining

The primary task of association rule mining is to detect frequently co-occurring groups of items in transactional databases. The intention is to use this knowledge for prediction purposes. So many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in and less attention has been paid to the methods that exploit these for prediction purposes. This paper contributes to the latter task by proposing a technique that uses the partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy, for example, If bread, butter, and milk often appear in the same item, then the presence of butter and milk in a shopping cart suggests that the customer may also buy bread. More generally knowing which items a shopping cart contains, we want to predict often items that the customer is likely to add before proceeding to the checkouts. So this paper presents a technique called the Matrix whose principal diagonal elements represent the association among items and looking to the principal diagonal elements, the customer can select what else the other items can be purchased with the currently contents of the shopping cart and also reduces the rule mining cost. The association among items is shown through Graph. The frequent itemsets are generated from the Combo Matrix. Then association rules are to be generated from the already generated frequent itemsets. The association rules generated form the basis for prediction. The incoming itemsets i.e. the contents of the shopping cart will be represented by set of unique indexed numbers and the association among items is generated through the Combo Matrix. Finally the predicted items are suggested to the Customer.

Mining for Mutually Exclusive Items in Transaction Databases

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

Mining association rules using inverted hashing and pruning

In this paper, we propose a new algorithm named Inverted Hashing and Pruning (IHP) for mining association rules between items in transaction databases. The performance of the IHP algorithm was evaluated for various cases and compared with those of two well-known mining algorithms, Apriori algorithm [Proc. 20th VLDB Conf., 1994, pp. 487–499] and Direct Hashing and Pruning algorithm [IEEE Trans. on Knowledge Data Engrg. 9 (5) (1997) 813–825]. It has been shown that the IHP algorithm has better performance for databases with long transactions.