Large Number Of Candidate Itemsets Research Articles

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints

High utility itemsets (HUIs) mining is one of the emerging topics in frequent itemset mining (FIM). HUIs mining provides more informative and actionable information compared to FIM. Although many HUIs mining algorithms have been proposed in recent years. They incur the problem of generating a large number of candidate itemsets and most of the generated itemsets are tiny in size which degrade mining performance and action-ability. Apart from these problems, most of the algorithms work only with positive utility value. To overcome these issues, we propose an algorithm named EHNL (Efficient High utility itemsets mining with Negative utility and Length constraints). Although negative utility and constraint-based mining are commonly seen in real-world applications, mining HUIs with negative utility and length constraints has not yet been proposed in literature. Most of the traditional algorithms suffer from multiple dataset scanning problem. To reduce the scanning cost, we utilize dataset projection and transaction merging techniques. To enhance the performance of the proposed algorithm, we utilize sub-tree based pruning technique. To check the efficiency of utilized techniques, the variations of the proposed algorithm named EHNL(RSUP) and EHNL(TM) are introduced. The experimental results show that variants of the proposed algorithm mine the HUIs efficiently.

Survey on High Utility Itemset Mining

Utility-based data mining is a new research area involved in all types of utility factors in data mining processes. The main objective of Utility Mining is to categorize the itemsets with highest utilities, by making an allowance for other user preferences such as quantity, profit and cost.Mining high utility itemsets from a transactional database refers to the encounter of itemsets with high utility like profits. It experience the problem of producing a large number of candidate itemsets used for high utility itemsets. Such a large number of candidate itemsets destroys the mining concert in terms of execution time and space requirement

Rup/Frup-growth: An Efficient Algorithm for Mining High Utility Itemsets

High Utility Itemsets Mining is a topic in the field of data mining. Many algorithms have been proposed in recent years, but they generate a large number of candidate itemsets for high utility itemsets. So the bottleneck of these algorithms is the processing of the candidates; the situation may become worse with the increasing of the number of long transaction itemsets or the decreasing of the minimum utility threshold. Meanwhile, in real-life marketing, the profit value of an item should also be considered along with its selling frequency and quantity. To address these issues, proposes an improvement of the UP-Growth algorithm called RUP-Growth, and develops a new algorithm called FRUP-Growth to take into consideration both the minimum support number and the minimum utility value to mine frequent & high utility itemsets. The experimental results show that our proposed strategies are more efficient and effective; especially with real-life marketing database, the advantage is more obvious.

High Utility Itemset Mining from TransactionDatabase Using UP-Growth and UP-Growth+Algorithm

Efficient discovery of itemsets with high utility like profits deals with the mining high utility itemsets from a transaction database Although a number of relevant approaches have been proposed in recent years, these algorithm incur the problem of producing a large number of candidate itemsets for high utility itemsets and probably degrades the mining performance in terms of execution time and memory space. In this paper, we propose two algorithms, viz., utility pattern growth (UP-Growth) and Improved UP-Growth i.e. Improved Utility Pattern Growth, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a compact tree-based data structure utility pattern tree (UP-Tree), it scan the original database twice to manage data structured way. Proposed algorithms, especially Improved UP Growth, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime and memory consumption, especially when databases contain lots of long transactions .

Efficient High Utility Itemset Mining using Utility Information Record

High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays an important role in many real life applications and is an important research issue in data mining area. In recent years, the problems of high utility pattern mining become one of the most important research areas in data mining. The existing high utility mining algorithm generates large number of candidate itemsets, which takes much time to find utility value of all candidate itemsets. In this paper we are implementing a data structure that stores the utility related to the item and using this data structure we are reducing time and space complexity of UP Growth and UP Growth+ Algorithms. Various Standard and synthetic datasets are used with Educational real data set. An algorithm is proposed to find set of high utility itemset which avoids the candidate itemsets generation.

UP-GNIV: an expeditious high utility pattern mining algorithm for itemsets with negative utility values

Traditionally, frequent pattern mining dealt in extracting frequency pattern from transaction databases by not considering utility factors. Utility-based data mining focuses on all aspects of economic utility in data mining and is aimed at incorporating utility in both predictive and descriptive data mining tasks. High utility itemset (HUI) mining process incurs the problem of producing a large number of candidate itemsets since downward closure property used in frequent itemset mining is not applied in utility mining and itemsets associated with negative utility values are not supported by existing algorithms. Here, mining high utility itemset with negative item values using Utility Pattern-Growth approach for Negative Item Values (UP-GNIV) approach is proposed and compared against high utility itemsets with negative item values (HUINIV)-mine. The experimental result shows that the suggested approach performs well.

Survey On: Mining High Utility Itemsets from Transactional Database

Utility mining emerges as an important topic in data mining field. Mining high utility itemsets from databases refers to finding the itemsets with high profits. The meaning of itemset utility is interestingness, importance, or profitability of an item to users. Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although several relevant algorithm has been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. This research work focuses on efficient method for high utility itemset mining, to reduce number of overestimated itemsets and to reduce the search space in order to improve the performance of high utility itemset mining. Key Terms: Candidate itemset, frequent itemset, high utility itemset, utility mining, data mining. I. Introduction Now a days computers are used widely in different areas. Fast reliable and unlimited secondary storage provides a perfect environment for the users to collect and store large amount of the data. Computers are also used to extract the useful information from the mass of data. This is called as the knowledge discovery or Data mining. Data mining is the process of revealing previously unknown and potentially useful information from large databases. The primary goal is to discover the hidden patterns in the data and to extract previously unknown interesting patterns. Discovering useful patterns hidden in a database plays an essential role in several data mining tasks, like frequent pattern mining and high utility pattern mining. Among them the fundamental research topic is frequent pattern mining which can be applied to different kinds of databases such as transactional database, streaming database and time series database and many applications domains like bioinformatics, web click-stream analysis and mobile environment.

BAHUI

Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In this paper, the authors propose an efficient algorithm, namely BAHUI (Bitmap-based Algorithm for High Utility Itemsets), for mining high utility itemsets with bitmap database representation. In BAHUI, bitmap is used vertically and horizontally. On the one hand, BAHUI exploits a divide-and-conquer approach to visit itemset lattice by using bitmap vertically. On the other hand, BAHUI horizontally uses bitmap to calculate the real utilities of candidates. Using bitmap compression scheme, BAHUI reduces the memory usage and makes use of the efficient bitwise operation. Furthermore, BAHUI only records candidate high utility itemsets with maximal length, and inherits the pruning and searching strategies from maximal itemset mining problem. Extensive experimental results show that the BAHUI algorithm is both efficient and scalable.

High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

High utility itemset mining considers the importance of items such as profit and item quantities in transactions. Recently, mining high utility itemsets has emerged as one of the most significant research issues due to a huge range of real world applications such as retail market data analysis and stock market prediction. Although many relevant algorithms have been proposed in recent years, they incur the problem of generating a large number of candidate itemsets, which degrade mining performance. In this paper, we propose an algorithm named MU-Growth (Maximum Utility Growth) with two techniques for pruning candidates effectively in mining process. Moreover, we suggest a tree structure, named MIQ-Tree (Maximum Item Quantity Tree), which captures database information with a single-pass. The proposed data structure is restructured for reducing overestimated utilities. Performance evaluation shows that MU-Growth not only decreases the number of candidates but also outperforms state-of-the-art tree-based algorithms with overestimated methods in terms of runtime with a similar memory usage.

Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.

An Improved UP-Growth High Utility Itemset Mining

Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP Tree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP Tree to the UP Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.

HUPT-mine: an efficient algorithm for high utility pattern mining

In recent years, the problem of high utility pattern mining becomes one of the most important research areas in data mining. High utility pattern mining extracts patterns which have utility value higher than or equal to user specified minimum utility. The problem is challenging, because of the non-applicability of anti-monotone property of frequent pattern mining. The existing high utility pattern mining algorithm adopts level wise candidate generation and many recently proposed approaches also generate large number of candidate itemsets. In this paper, a novel high utility pattern tree (HUPT) is proposed by applying two pruning strategies to reduce number of candidate itemsets by scanning database twice. For each conditional pattern base, a local tree is constructed with required information to generate candidate itemsets, by employing pattern growth approach. The experimental results on different datasets show that it reduces the number of candidate itemsets and also outperforms two-phase algorithm for dense datasets with long transactions.

MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams

Online mining of utility itemsets from data streams is one of the most interesting research issues in stream data mining. Although a number of relevant approaches have been proposed in recent years, they have the drawback of producing a large number of candidate itemsets for high-utility itemset mining. In this paper, an efficient algorithm, called MHUI-max (Mining High-Utility Itemsets based on LexTree-maxHTU), is proposed for mining high-utility itemsets from data streams with fewer candidates. Based on the framework of the MHUI-max algorithm, an effective representation of item information, called TID-list, and a new lexicographical tree-based data structure, called LexTree-maxHTU, has been developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithm, MHUI-max, outperforms the existing approaches, MHUI-TID and THUI-Mine, for mining high-utility itemsets from data streams over transaction-sensitive sliding windows.

A Web Data Mining Algorithm Based on Weighted Association Rules

Association rules mining is attracting much attention in research community due to its broad applications. Existing web data mining methods suffer the problems that 1) the large number of candidate itemsets, which are hard to be pruned, should be pruned in advance. 2) the time of scanning the database, which are needed to scan transactional database repeatedly, should be reduced. In this paper, a new association rules mining model is introduced for overcoming above two problems. We develop an efficient algorithm-WARDM(Weighted Association Rules Data Mining) for mining the candidate itemsets. The algorithm discusses the generation of candidate-1 itemset, candidate-2 itemset and candidate-k itemset(k>2),which can avoid missing weighted frequent itemsets. And the transactional database are scanned only once and candidate itemsets are pruned twice, which can reduce the amount of candidate itemsets. Theoretical analysis and experimental results show the space and time complexity is relatively good, Meanwhile the algorithm decreases the number of candidate itemsets, enhances the execution efficiency.

Research and improvement on Apriori algorithm of association rule mining

The classic Apriori algorithm for discovering frequent itemsets scans the database many times and the pattern matching between candidate itemsets and transactions is used repeatedly, so a large number of candidate itemsets were produced, which results in low efficiency of the algorithm. The improved Apriori algorithm improved it from three aspects: firstly, the strategy of the join step and the prune step was improved when candidate frequent (k+1)-itemsets were generated from frequent k-itemsets; secondly, the method of dealing with transaction was improved to reduce the time of pattern matching to be used in the Apriori algorithm; in the end, the method of dealing with database was improved, which lead to only once scanning of the database during the whole course of the algorithm. According to these improvements, an improved algorithm was introduced. The efficiency of Apriori algorithm got improvement both in time and in space. The experimental results of the improved algorithm show that the improved algorithm is more efficient than the original.