Candidate Itemsets Research Articles

Fast Vertical Mining Using Boolean Algebra

The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The sizes of bit vectors for itemsets are the main space expense of the algorithm that restricts its expansibility. Therefore, in this paper, a proposed algorithm that compresses the bit vectors of frequent itemsets will be presented. The new bit vector schema presented here depends on Boolean algebra rules to compute the intersection of two compressed bit vectors without making any costly decompression operation. The experimental results show that the proposed algorithm, Vertical Boolean Mining (VBM) algorithm is better than both Apriori algorithm and the classical vertical association rule mining algorithm in the mining time and the memory usage.

Association Rules Algorithm Based on the Intersection

Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mining the maximum frequent patterns. Firstly, applying the intersection theory of mathematics, put forwards a number of concepts and definitions. Then gives the process of association rules mining, and analyzes its performance. After that, the example describes the process of implementation of the algorithm. Finally, the experimental results show that the algorithm ISMFP is efficient on mining frequent patterns, especially there exists low threshold of support degree or long patterns.

SET-PSO-based approach for mining positive and negative association rules

Data mining is the process of determining new, unanticipated, valuable patterns from existing databases by considering historical and recent developments in statistics, artificial intelligence, and machine learning. It can help companies focus on the most important information in their data warehouses. Association rule mining is one of the most highly researched and popular data mining techniques for finding associations between items in a set. It is frequently used in marketing, advertising, and inventory control. Typically, association rules only consider items in transactions (positive association rules). They do not consider items that do not occur together, which can be used to create rules that are also useful for market basket analysis. Also, existing algorithms often generate too many candidate itemsets when mining the data and scan the database multiple times. To resolve these issues in association rule mining algorithms, we propose SARIC (set particle swarm optimization for association rules using the itemset range and correlation coefficient). Our method uses set particle swarm optimization to generate association rules from a database and considers both positive and negative occurrences of attributes. SARIC applies the itemset range and correlation coefficient so that we do not need to specify the minimum support and confidence, because it automatically determines them quickly and objectively. We verified the efficiency of SARIC using two differently sized databases. Our simulation results demonstrate that SARIC generates more promising results than Apriori, Eclat, HMINE, and a genetic algorithm.

Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.

English

Now a days high utility item sets specially from large transaction databases is required task to process many day to day operations in quick time. In many relevant algorithms presented those are surface the problem of generating large number of candidate item set and thus degrades the mining performance in terms of execution time and space. In this paper mainly three algorithms are presented, such as UP-Growth (utility pattern) for mining high utility item set for pruning candidate item sets. In these algorithms, compact tree structure (UP-Tree) is used for discovering the useful item-set so that candidate item is generated with only two scan of database. The performance of UP-Growth was evaluated in comparison with the state-of-theart algorithms on different types of datasets. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. If we did not use one of the above algorithm then we will face number of problems like if we are retrieve items then that operation takes more time to scan database. It is a time consuming process. This process reduces performance of the project. So we are prefer other alternatives like using UP growth algorithm, FP growth algorithm etc. In these algorithms also have some drawbacks like time consuming i.e. scans database for multiple times. So all aspects we considered and proposed one algorithm in this project i.e. UP Growth algorithm. It is very efficient algorithm for mining high utility compare to FP growth algorithm and etc.

Discovering Frequent Itemsets an Improved Algorithm of Bit Vector and Graph

Because of the weakness of traditional Apriori algorithm, this paper presents an improved algorithm for mining frequent itemsets, which constructs bit vector and graph, the algorithm deletes node and the adjacent edges according to the number of node’s edges, which need traverse graph to generate candidate itemsets and verify candidate itemset by bit vector. Experimental results show that the improved algorithm has better efficiency than Apriori algorithm.

Improved Apriori Algorithm in the Power System Fault

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

The New Algorithms of Weighted Association Rules based on Apriori and FP-Growth Methods

In order to improve the frequent itemsets generated layer-wise efficiency, the paper uses the Apriori property to reduce the search space. FP-grow algorithm for mining frequent pattern steps mainly is divided into two steps: FP-tree and FP-tree to construct a recursive mining. Algorithm FP-Growth is to avoid the high cost of candidate itemsets generation, fewer, more efficient scanning. The paper puts forward the new algorithms of weighted association rules based on Apriori and FP-Growth methods. In the same support, this method is the most effective and stable maximum frequent itemsets mining capacity and minimum execution time. Through theoretical analysis and experimental simulation of the performance of the algorithm is discussed, it is proved that the algorithm is feasible and effective. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4770

Maintenance of prelarge trees for data mining with modified records

The frequent pattern tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It is used to compress a database into a tree structure which stores only large items. When data are modified, it, however, needs to process all transactions in a batch way. In the past, the prelarge-tree structure was proposed to incrementally mine association rules efficiently. In this paper, we propose an algorithm to maintain this structure when records in an original database are modified. The proposed maintenance algorithm is based on the pre-large concepts, which are defined by a lower support threshold and an upper support threshold. Due to the pruning properties of pre-large concepts, the proposed approach can reduce the rescan number of an original database when records are modified. It can thus obtain good execution performance for pre-large tree maintenance, especially when each time a small number of records are modified. Although experimental results show that the proposed prelarge-tree maintenance algorithm has good performance for handling modified records, the proposed algorithm needs to maintain nodes of pre-large items in the tree structure. This is the additional overhead, which is a trade-off between execution time and tree complexity.

Research on Parallel Association Rules Mining Algorithm Based on Hadoop

The purpose of association rules mining is to find rules which can meet the minimum support and minimum confidence from a large quantity of data. To find the valid association rules efficiently, we had a comprehensive analysis on some well-know parallel association rules mining algorithms and proposes a new parallel association rules mining algorithm (Array Based on Hadoop, short for ABH) based on the cloud computing platform. The ABH scans the database only once, uses the 0/1 array to represent one of the transactions and to record the frequency of the same transaction. Moreover, by utilizing the random access characteristics of the array and the special nature of the frequent itemset, the ABH can reduce the quantity of frequent candidate itemset effectively and find the frequent itemset quickly. We have compared the ABH with two classical algorithms CD and DD through experiment; we can find that ABH outperforms CD and DD.

A frequent itemset mining algorithm based on the Principle of Inclusion–Exclusion and transaction mapping

This paper proposes a novel frequent itemsets mining algorithm called PIETM (Principle of Inclusion–Exclusion and Transaction Mapping). PIETM has three major features. First, similar to the mining process in Apriori, PIETM discovers frequent itemsets in a bottom-up manner. However, it reduces database scanning to only two times. Second, PIETM does not scan the database to count the itemsets’ support. Instead, it employs the Principle of Inclusion–Exclusion to calculate the support of candidate itemsets. Third, PIETM uses transaction intervals to map and store the transaction ids of each item, which facilitates the itemsets counting process. We also present experimental results comparing PIETM with existing algorithms in this paper. The results show that PIETM takes lower execution time than other methods when the dataset has numerous items. In summary, this paper makes three major contributions. First, it presents a new method that calculates the support of itemsets using a well-known set theory property. Second, it demonstrates the correctness of counting itemsets in PIETM. Third, our method demonstrates its suitability for different high-performance applications by combining the advantages of the simplicity of Apriori and efficiency of FP-growth.

Use of cognitive interviews in the development of the PLUS-M item bank.

Measuring constructs such as mobility with patient-reported outcomes (PROs) can enhance clinical and scientific understanding of how health conditions, like lower limb amputation, impact patients' lives. When developing PRO questionnaires, cognitive interviews (CIs) are used to examine whether survey items are understandable, clear, and meaningful. The aim of this study was to use CIs to inform item development for the Prosthetic Limb Users Survey of Mobility (PLUS-M), a PRO that measures mobility in prosthetic limb users. Thirty-six CIs were conducted with 30 prosthetic limb users. Each participant responded to up to 30 items from the PLUS-M candidate item set. Each item was reviewed by a minimum of five participants who differed in self-reported mobility, literacy, level of amputation, and time since amputation. Items were revised based on participant feedback, and substantially revised items were re-evaluated through additional CIs. Feedback from CIs identified substantial issues in 76 of the total 156 items. These items were subsequently modified or eliminated. Cognitive interviews were an essential qualitative step in the development of the PLUS-M item bank and resulted in better functioning items.

An Algorithm of Top-k High Utility Itemsets Mining over Data Stream

Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k HUIs mining algorithm TOPK-SW; it first stores each batch data of current window as well as the items' utility information to a tree called HUI-Tree, which ensures effective retrieval of utility values without re-scan the dataset, so as to efficiently improve the mining performance. TOPK-SW was tested on 4 classical datasets; results show that TOPK-SW outperforms existing algorithms significantly in both time and space efficiency, especially the time performance improves over 1 order of magnitude.

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.

Utility Mining Algorithm for High Utility Item sets from Transactional Databases

The discovery of item sets with high utility like profits is referred by mining high utility item sets from a transactional database. Although in recent years a number of relevant algorithms have been proposed, for high utility item sets the problem of producing a large number of candidate item sets is incurred. The mining performance is degraded by such a large number of candidate item sets in terms of execution time and space requirement. When the database contains lots of long transactions or long high utility item sets the situation may become worse. Internet purchasing and transactions is increased in recent years, mining of high utility item sets especially from the big transactional databases is required task to process many day to day operations in quick time. There are many methods presented for mining the high utility item sets from large transactional datasets are subjected to some serious limitations such as performance of this methods needs to be investigated in low memory based systems for mining high utility itemsets from large transactional datasets and hence needs to address further as well. Another limitation is these proposed methods cannot overcome the screenings as well as overhead of null transactions; hence, performance degrades drastically. During this paper, we are presenting the new approach to overcome these limitations. We presented distributed programming model for mining business-oriented transactional datasets by using an improved Map Reduce framework on Hadoop, which overcomes not only the single processor and main memory-based computing, but also highly scalable in terms of increasing database size. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. In experimental studies we will compare the performances of existing algorithms UP-Growth and UP-Growth+ against the improve UP-Growth and UP-Growth+ with Hadoop.

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.

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

Utility-based data mining is a new research area interested in all types of utility factors in data mining processes [1]. The basic meaning of utility is the quantity sold, interest, importance & profitability of items to the users. Utility of items in a transaction database consists of two aspects: 1. The importance of distinct or unique items, which is called external utility. 2. The importance of the items in the transaction, w is called as internal utility. Mining high utility itemsets from the databases is not an easy task. Pruning search space for high utility itemset mining is difficult because a superset of a low utility itemset may be a high utility itemset. Existing studies [2,4,9] applied overestimated methods to facilitate the mining performance of utility mining. In these methods, first we will get potential high utility itemsets, and then an additional database scan is performed for identifying their utilities. However, the existing methods often generate a huge candidate itemsets and the mining performance is degraded consequently. In this paper we proposed Eliminating Unusual Itemset by Eliminating item set which is low utility item set to reduce search space. Proposed methods not only reduce the number of candidate itemsets, but also significantly increase the performance of the mining process.

Mining Frequent Items in Uncertain Dataset

Because of uncertainty data, traditional algorithm of mining frequent items in certain dataset is difficult to apply to uncertain dataset. Considering characteristics of uncertain data, an improved vertical mining algorithm to find frequent items in uncertain dataset was proposed with the algorithm thought of classic vertical algorithm-Eclat in certain dataset. The improved algorithm merged TID field and corresponding probability field into probability vector. During the expansion of itemset and probability vector, itemset tree was established, and the support of candidate itemsets was calculated by means of vector operations. The improved algorithm is proved to be feasible and efficient according to experimental comparison and analysis.

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.