An algorithm for maximal frequent itemset mining from large databases using bit-set representation scheme

Discovering frequent itemsets is a key problem in important data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up breadth-first search direction. The computation starts from frequent 1-itemsets (minimal length frequent itemsets) and continues until all maximal (length) frequent itemsets are found. During the execution, every frequent itemset is explicitly considered. Such algorithms perform reasonably well when all maximal frequent itemsets are short. However, performance drastically decreases when some of the maximal frequent itemsets are relatively long. We present a new algorithm which combines both the bottom-up and top-down directions. The main search direction is still bottom-up but a restricted search is conducted in the top-down direction. This search is used only for maintaining and updating a new data structure we designed, the maximum frequent candidate set. It is used to prune candidates in the bottom-up search. As a very important characteristic of the algorithm, it is not necessary to explicitly examine every frequent itemset. Therefore it performs well even when some maximal frequent itemsets are long. As its output, the algorithm produces the maximum frequent set, i.e., the set containing all maximal frequent itemsets, which therefore specifies immediately all frequent itemsets. We evaluate the performance of the algorithm using a well-known benchmark database. The improvements can be up to several orders of magnitude, compared to the best current algorithms.

The association rule mining can be divided into two steps. The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold. The second step is to generate reliable association rules based on all frequent itemsets found in the first step. Identifying all frequent itemsets in a large database dominates the overall performance in the association rule mining. In this paper, we propose an efficient hash-based method, HMFS, for discovering the maximal frequent itemsets. The HMFS method combines the advantages of both the DHP (Direct Hashing and Pruning) and the Pincer-Search algorithms. The combination leads to two advantages. First, the HMFS method, in general, can reduce the number of database scans. Second, the HMFS can filter the infrequent candidate itemsets and can use the filtered itemsets to find the maximal frequent itemsets. These two advantages can reduce the overall computing time of finding the maximal frequent itemsets. In addition, the HMFS method also provides an efficient mechanism to construct the maximal frequent candidate itemsets to reduce the search space. We have implemented the HMFS method along with the DHP and the Pincer-Search algorithms on a Pentium III 800 MHz PC. The experimental results show that the HMFS method has better performance than the DHP and the Pincer-Search algorithms for most of test cases. In particular, our method has significant improvement over the DHP and the Pincer-Search algorithms when the size of a database is large and the length of the longest itemset is relatively long.

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

Frequent itemsets (FIs) mining from uncertain database is a very popular research area nowadays. Many algorithms have been proposed to mine FI from uncertain database. But in typical FI mining process, all the FIs have to be mined individually, which needs a huge memory. Four trees are proposed in this paper which are: (i) maximal frequent itemset from uncertain database (MFU) tree which contains only the maximal frequent itemsets generated from uncertain database, (ii) closed frequent itemset from uncertain database (CFU) tree which contains only closed frequent itemsets generated from uncertain database, (iii) maximal frequent itemset from uncertain data stream (MFUS) tree which contains maximal frequent itemsets generated from uncertain data stream and (iv) closed frequent itemset from uncertain data stream (CFUS) tree which contains closed frequent itemsets generated from uncertain data stream. Experimental results are also presented which show that maximal and closed frequent itemsets mining requires less time and memory than typical frequent itemsets mining.

Maximal frequent item sets are one of several condensed representations of frequent item sets, which store most of the information contained in frequent item sets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent item sets approximately over a stream landmark model. We separate the continuously arriving transactions into sections, and the mining results are indexed by an extended direct update tree, thus, a simple but effective algorithm named SMIS is proposed. In our algorithm, we employ the Chern off Bound to perform the maximal frequent item set mining in a false negative manner, which can reduce the memory cost, as well guarantee our algorithm conducting with an incremental fashion. Our experimental results on two synthetic datasets and two real world datasets show that SMIS achieves much reduced memory cost in comparison with the state-of-the-art algorithm with a 100 percent precision.

In the discipline of data mining, association rule mining is an important study topic that focuses on discovering the relationships between database attributes. The maximum frequent itemset comprises the information of all frequent itemsets, which is one of the important difficulties in mining association rules, and certain data mining applications just need to mine the maximum frequent itemsets. As a result, analyzing the maximum frequent itemset mining technique is practical. Considering this, the research introduces FP-MFIA, a new maximum frequent itemset mining approach based on the FP-tree, which is inspired by the data structure of the frequent pattern tree and the idea that the maximum frequent itemset implies all frequent itemsets. First, the FP-MFIA constructs a one-way FP-tree structure, which only has pointers from the root to the leaves, so that only two scans of the FP-tree are required by the FP-MFIA. On the other hand, it redefines a data storage structure MFI-list for maximum frequent itemsets. It can quickly release unnecessary nodes in the FP-tree after scanning it. In this way, not only the information required by the maximum frequent itemsets can be quickly mined but also the space required for storing the maximum frequent itemsets can be reduced, which greatly improves the mining efficiency. Finally, experiments were conducted to compare the mining efficiency of the novel FP-MFIA algorithm to the IDMFIA and DMFIA algorithms. We can see from the findings that the FP-MFIA algorithm is more efficient than the other two techniques.

Data mining has made a significant impact on business and knowledge management in recent years. Data mining also known as KDD is a powerful new technology having great potential to analyze useful information stored in large data bases. Association rule mining is an active research area, used to find association and/or correlation among frequent item sets. Association rule mining discover frequent item sets with out considering total cost, quantity and number of items in a transaction. Utility based frequent item set mining is a new research area, which provide importance to sale quantity and price among items in a transaction. In this paper, we have proposed a novel approach for utility frequent item set mining. Our method mines novel frequent item sets by giving importance to items quantity, significance weightage, utility and user defined support. Our approach can be used to provide valuable recommendation to the enterprise to improve business utility.

Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Most existing work focuses on mining all frequent itemsets. However, since any subset of a frequent set also is frequent, it is sufficient to mine the set of frequent closed itemsets which determines exactly the complete set of all frequent itemsets and is usually much smaller than the laster. In this paper, we study the performance of the existing approaches for mining frequent closed itemsets. We also develop an algorithm, called FCFIA. In this algorithm, we develop and integrate two techniques in order to improve the efficiency of mining frequent closed itemsets. We also present experimental results which show that our method outperforms the existing methods FPclose.

Mining frequent itemsets in databases is an important and widely studied problem in data mining research. The problem of mining frequent itemsets is usually solved by constructing candidates of itemsets, and identifying those itemsets that meet the requirement of frequent itemsets. This paper proposes a novel algorithm based on BitTable (or bitmap) representation of the data. Data - related to frequent itemsets - are stored in spare matrices. Simple matrix and vector multiplications are used to calculate the support of the potential n+1 itemsets. The main benefit of this approach is that only bitmaps of the frequent itemsets are generated. The concept is simple and easily interpretable and it supports a compact and effective implementation (in MATLAB). An application example related to the BMS-WebView-1 benchmark data is presented to illustrate the applicability of the proposed algorithm.

Association rules mining is one of the most important problems in knowledge discovery and data mining. The goal of it is to acquire consumption habits of customers by discovering the relationships between items from a transaction database that has a large number of transactions and items. The most compute intensive process for ARM is to find out the frequent 1-itemsets and 2-itemsets. In this paper, we propose a quantum algorithm for finding out the frequent 1-itemsets and 2-itemsets. In our algorithm, to mine the frequent 1-itemsets efficiently, we use the technique of amplitude amplification. To mine the frequent 2-itemsets efficiently, we propose a new tomography scheme, i.e., pure-state-based quantum state tomography. It is shown that our algorithm is potential to achieve exponential speedup in the number of transactions and polynomial speedup in the number of items over the classical algorithm.

Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets.

Frequent itemset mining is an important problem in the data mining area. Extensive efforts have been devoted to developing efficient algorithms for mining frequent itemsets. However, not much attention is paid on managing the large collection of frequent itemsets produced by these algorithms for subsequent analysis and for user exploration. In this paper, we study three structures for indexing and querying frequent itemsets: inverted files, signature files and CFP-tree. The first two structures have been widely used for indexing general set-valued data. We make some modifications to make them more suitable for indexing frequent itemsets. The CFP-tree structure is specially designed for storing frequent itemsets. We add a pruning technique based on length-2 frequent itemsets to make it more efficient for processing superset queries. We study the performance of the three structures in supporting five types of containment queries: exact match, subset/superset search and immediate subset/superset search. Our results show that no structure can outperform other structures for all the five types of queries on all the datasets. CFP-tree shows better overall performance than the other two structures.

Bioinformatics has contributed to a different form of datasets called as high dimensional datasets. The high dimensional datasets are characterized by a large number of features and a small number of samples. The traditional algorithms expend most of the running time in mining large number of small and mid-size items which does not enclose valuable and significant information. The recent research focused on mining large cardinality itemsets called as colossal itemsets which are significant to many applications, especially in the field of bioinformatics. The existing frequent colossal itemset mining algorithms are unsuccessful in discovering complete set of significant frequent colossal itemsets. The mined colossal itemsets from existing algorithms provide erroneous support information which affects association analysis. Mining significant frequent colossal itemsets with accurate support information helps in attaining a high-level accuracy of association analysis. The proposed work highlights a novel pre-processing technique and bottom-up row enumeration algorithm to mine significant frequent colossal itemsets with accurate support information. A novel pre-processing technique efficiently utilizes minimum support threshold and minimum cardinality threshold to prune irrelevant samples and features. The experiment results demonstrate that the proposed algorithm has high accuracy over existing algorithms. Performance study indicates the efficiency of the pre-processing technique.

Frequent pattern mining is the most important phase of association rule mining process because of its time and space complexity. Several methods have attempted to improve the performance of association rule mining by enhancing frequent pattern mining efficiency. Due to the large size of the data-sets and huge amounts of data which should be mined, many parallel and distributed mining approaches have been introduced to divide data-sets or to distribute mining processes between multiple processors or computers and thus, improve the efficiency of the mining process. In this paper, we propose a hadoop-based parallel implementation of PrePost+ algorithm for frequent itemset mining. In our parallel approach, the process of constructing N-Lists of itemsets has been distributed between the mappers and the operation of the final pruning process and extracting frequent itemsets has been carried out by reducers in a map-reduce parallel programming model. The experimental results show that our hadoop-based PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel methods of frequent itemset mining (PARMA) in terms of execution time.

Finding the frequent itemset is one of the most investigated extents of data mining. The Apriori algorithm is the most established algorithm for frequent itemset mining, but it has issues regarding scanning frequent databases and generating a large amount of candidate sets. To solve these issues, an Improved Apriori algorithm was proposed. We examined the data structure, implementation, and algorithmic features that mainly focus on frequent itemset mining. We are representing an Improved Apriori algorithm on Spark in which simple and scalable implementation is done to achieve a faster process with lower support thresholds. We examined the improved Apriori algorithm on Extended Bakery Dataset and Retail Dataset. The results show execution time was reduced by 40% and 57% compared with the original Apriori algorithm.