Frequent Itemset Mining Research Articles

A regression-based algorithm for frequent itemsets mining

PurposeFrequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method.Design/methodology/approachRegression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items.FindingsSome interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information.Research limitations/implicationsThe proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining.Practical implicationsCompared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations.Originality/valueThe proposed algorithms provide a novelty view to define and mine association.

Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

A non-group parallel frequent pattern mining algorithm based on conditional patterns

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.

FPO tree and DP3 algorithm for distributed parallel Frequent Itemsets Mining

Frequent Itemsets Mining is a fundamental mining model in Data Mining. It supports a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Many distributed parallel algorithms have been introduced to confront with very large-scale datasets of Big Data. However, the problems of running time and memory scalability still have not had adequate solutions for very large and “hard-to-mined” datasets. In this paper, we propose a distributed parallel algorithm named DP3 (Distributed PrePostPlus) which parallelizes the state-of-the-art algorithm PrePost+ and operates in Master-Slaves model. Slave machines mine and send local frequent itemsets and support counts to the Master for aggregations. In the case of tremendous numbers of itemsets transferred between the Slaves and Master, the computational load at the Master, therefore, is extremely heavy if there is not the support from our complete FPO tree (Frequent Patterns Organization) which can provide optimal compactness for light data transfers and highly efficient aggregations with pruning ability. Processing phases of the Slaves and Master are designed for memory scalability and shared-memory parallel in Work-Pool model so as to utilize the computational power of multi-core CPUs. We conducted experiments on both synthetic and real datasets, and the empirical results have shown that our algorithm far outperforms the well-known PFP and other three recently high-performance ones Dist-Eclat, BigFIM, and MapFIM.

Partition based Single Scan Method for Mining Frequent Item Sets

Frequent Itemset mining (FIM) concept and limitations are explored in this paper, for the purpose of extracting unknown hidden patterns as itemsets from the transactional database. Since candidate generation and support calculations are the major tasks in FIM, the major limitations of FIM are tackled, (i) huge possible frequent itemsets are generated as candidates at each pass (ii) Data base scan at each pass to calculate the support of the generated itemsets (iii) generated itemsets are highly sensitive to the minimum support threshold. SS-FIM a single scan algorithm is to deal with the above limitations. However, several unnecessary itemsets are being hashed in the buckets. To overcome the limitations, a partition based approach is proposed in this paper. The proposed approach, PSSFIM, takes single scan of the database to identify frequent itemsets. The unique feature of PSSFIM allow to generate size of candidate itemsets independent on the minimum support. It allows the candidates in hash that are possible for frequent, which intuitively reduces the cost in terms of verifying the support of generated candidates. It is compared with SS-FIM and Apriori with the standard datasets. The results show that the PSSFIM is good at the comparison of SS-FIM and Apriori.

Mining Closed Item sets from Tuple-Evolving Data Streams

Frequent Itemset Mining is playing major role in extracting useful knowledge from data streams that are exhibiting high data flow. Studies in data streams shows that every incoming data is considered as new tuple which is considered as revised tuple in some applications called as tuple evolving data streams. Extracting redundant less knowledge from such kind of application helps in better decision making with new challenges.One of the issue is, due to incoming revised tuple, some of the frequent itemsets may turn to infrequent or previously ignore itemsets may become frequent. Other issue is result of FIM may be huge and redundant results.In this paper, we address solution to the problem by finding closed itemsets from tuple revision data streams. We propose an efficient approach MCST that uses compressed SlideTree data structure to maintain stream data,proposeHIS hash tableto maintain itemsets, and CIS tables to maintain closed id sets to improve search performance of HIS.

Improved Frequent Item-Sets Mining in Pharmacovigilance

Mining frequent item-sets is an important concept that deals with fundamental and initial task of data mining. Apriori is the most popular and frequently used algorithm for finding frequent item-sets which is preferred over other algorithms like FP-growth due to its simplicity. For improving the time efficiency of Apriori algorithms, Jiemin Zheng introduced Bit-Apriori algorithm with the enhancement of support count and special equal support pruning with respect to Apriori algorithm. In this paper, a novel Bit-Apriori algorithm, that deletes infrequent items during trie2 and subsequent tries are proposed which can be used in pharmacovigilance to identify the adverse event

Positive and negative association rule mining in Hadoop\u2019s MapReduce environment

In this paper, we present a Hadoop implementation of the Apriori algorithm. Using Hadoop’s distributed and parallel MapReduce environment, we present an architecture to mine positive as well as negative association rules in big data using frequent itemset mining and the Apriori algorithm. We also analyze and present the results of a few optimization parameters in Hadoop’s MapReduce environment as it relates to this algorithm. The results are presented based on the number of rules generated as well as the run-time efficiency. We find that, a higher amount of parallelization, which means larger block sizes, will increase the run-time efficiency of the Hadoop implementation of the Apriori algorithm.

Mining high occupancy itemsets

Frequent itemset mining has been extensively studied in data mining for over the last two decades because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world applications, such as the travel landscapes recommendation, where occupancy besides support also plays a key role in evaluating the interestingness of an itemset. In this paper, we propose a new kind of tasks based on occupancy, namely high occupancy mining, by introducing occupancy into the support-based mining framework. An efficient algorithm, HEP (abbreviation for High Efficient algorithm for mining high occupancy itemsets), is developed to discover all high occupancy itemsets. HEP use a structure, named occupancy-list, to store the occupancy information about an itemset and employs an iterative level-wise approach to mine high occupancy itemset via a pruning strategy based on upper bound of occupancy. Substantial experiments on both synthetic and real datasets show that HEP is efficient for mining high occupancy itemsets and is at least one order of magnitude faster than the baseline algorithm.

A data structure perspective to the RDD-based Apriori algorithm on Spark

During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers. Initially, MapReduce-based frequent itemset mining algorithms on Hadoop cluster were proposed. Although, Hadoop has been developed as a cluster computing system for handling and processing big data, but the performance of Hadoop does not meet the expectation for the iterative algorithms of data mining, due to its high I/O, and writing and then reading intermediate results in the disk. Consequently, Spark has been developed as another cluster computing infrastructure which is much faster than Hadoop due to its in-memory computation. It is highly suitable for iterative algorithms and supports batch, interactive, iterative, and stream processing of data. Many frequent itemset mining algorithms have been re-designed on the Spark, and most of them are Apriori-based. All these Spark-based Apriori algorithms use Hash Tree as the underlying data structure. This paper investigates the efficiency of various data structures for the Spark-based Apriori. Although, the data structure perspective has been investigated previously, but for MapReduce-based Apriori, and it must be re-investigated in the distributed computing environment of Spark. The considered underlying data structures are Hash Tree, Trie, and Hash Table Trie. The experimental results on the benchmark datasets show that the performance of Spark-based Apriori with Trie and Hash Table Trie are almost similar but both perform many times better than Hash Tree in the distributed computing environment of Spark.

A MapReduce-Based Parallel Mining for Frequent Itemset

Frequent itemset mining is the most important step of association rule mining. Due to the large size of datasets, many parallel mining methods have been introduced to divide datasets or to distribute mining processes, which improve the efficiency of mining. In this paper, we propose a parallel algorithm of PrePost+ based on MapReduce (we call our parallel algorithm MR-PrePost+) to mine frequent itemsets. In our parallelization approach, the Reduce task performs a set of independent mining tasks, which eliminates data dependencies and computational dependencies between machines. The experimental results show that our MapReduce-based parallel algorithm MR-PrePost+ performs better than PFP in running time, which is one of the best frequent itemset mining parallel algorithms.

Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data

By the recent developments about the information technologies, companies can store their data faster and easier with lower costs. All transactions (sales, current card, invoicing, etc.) performed in companies during the day combine at the end of the day to form big datasets. It is possible to extract valuable information through these datasets with data mining. And this has become more important for companies in terms of today's conditions where the competition in the market is high. In this study, a dataset of a company selling car maintenance and repair products in Turkey is used. Association Rules are applied on this dataset for determining the items which are bought together by the customers. These rules, which are calculated specifically for the company, can be used to redefine the sales and marketing strategies, to revise the storage areas efficiently, and to create sales campaigns suitable for the customers and regions. These algorithms are also called Frequent Itemset Mining Algorithms. The most recent 11 algorithms from these are applied to this dataset in order to compare the performances according to metrics like memory usage and execution times against varying support values and varying record numbers by using SPMF platform. Three different datasets are created by using the whole dataset like 6-months, 12-months and 22-months. According to the experiments, it can be said that executon times generally increases inversely with the support values as nearly all algorithms have higher execution time values for the lowest support value of 0.1. dEclat_bitset algorithm has the most efficient performance for 6-months and 12-months dataset. But Eclat algorithm can be said to be the most efficient algorithm for 0.7 and 0.3 support values; on the other hand dEclat_bitset is the most efficient algorithm for 0.3 and 0.1 support values on 22-months dataset.

Enhancing the Performance of Large-scale Profitable Itemset Mining using Efficient Data Structures

The process of extracting the most frequently bought items from a transactional database is termed as frequent itemset mining. Although it provides us with an idea of the best-selling itemsets, the method fails to identify the most profitable items from the database. It is not uncommon to have minimal intersection between frequent itemsets and profitable itemsets, and the process of extracting the most profitable itemsets is termed as Greater Profitable Itemset (GPI) mining. There have been various approaches to mine GPI in which [7] proposed a two-phased algorithm to optimize regeneration of GPI when the profit value of any item changes. This constituted of keeping track of the pruned items in the first phase and using it to efficiently regenerate GPI in the second phase. This paper proposes an enhancement to the way these changes are tracked by storing the pruned itemsets according to their constituent items, unlike the earlier algorithm that stored records iteration wise. By storing the itemsets according to their constituent items, we make sure that only the required items are being retrieved. In contrast, the earlier algorithm would fetch all the items pruned in any iteration, regardless of its relevance. By fetching only relevant itemset, the proposed method would significantly bring down the computational requirements.

An Efficient R-Apriori Algorithm for Frequent Item set Mining in Python

The mining of affiliation rules remains a well-enjoyed and successful procedure for getting critical data from monstrous data sets. It attempts to look out feasible connections between things in monstrous data sets upheld exchanges. Visit examples ought to be created to frame these affiliations. The "R-APRIORI" standard and its arrangement of improved variations, that were one in all the soonest visit design age calculations arranged, remain a most well known option because of their easy to execute and parallel to the common inclination. despite the fact that there are a few conservative single-machine methodologies for Apriori, the huge amount of data by and by open so much surpasses the capacity of 1 machine. In this way, it's important to scale over numerous machines to satisfy the regularly developing requests of this data. Guide cut back could be a well-loved distributable adaptation to non-critical failure structure. Be that as it may, genuine circle I/O in each Map cut back activity obstructs the efficient usage unvaried Map cut back information handling calculations like Apriori Platforms. An as of late arranged distributable data stream stage Sparkle beats the Map cut back I/O circle bottlenecks. Shimmer so gives an ideal stage to circulation Apriori. In any case, the principal computationally costly errand inside the execution of Apriori is to thought of applicant sets with everysingle possible go after singleton visit things and to check each match with each managing record. Here we tend to propose a spic and span approach that drastically decreases this methodology multifaceted nature by dispensing with the progression of creating applicants and maintaining a strategic distance from costly examinations. We stock out in– profundity trials to discover the power and quantifiability of our methodology. Our investigations demonstrate that our methodology commonly beats Sparkle'sexemplary Apriori and dynamic for different data sets.

Extending inverse frequent itemsets mining to generate realistic datasets: complexity, accuracy and emerging applications

The development of novel platforms and techniques for emerging “Big Data” applications requires the availability of real-life datasets for data-driven experiments, which are however not accessible in most cases for various reasons, e.g., confidentiality, privacy or simply insufficient availability. An interesting solution to ensure high quality experimental findings is to synthesize datasets that reflect patterns of real ones using a two-step approach: first a real dataset X is analyzed to derive relevant patterns Z (latent variables) and, then, such patterns are used to reconstruct a new dataset $$X'$$ that is like X but not exactly the same. The approach can be implemented using inverse mining techniques such as inverse frequent itemset mining ( $$\texttt {IFM}$$ ), which consists of generating a transactional dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. This paper introduces various extensions of $$\texttt {IFM}$$ within a uniform framework with the aim to generate artificial datasets that reflect more elaborated patterns (in particular infrequency and duplicate constraints) of real ones. Furthermore, in order to further enlarge the application domain of $$\texttt {IFM}$$ , an additional extension is introduced that considers more structured schemes for the datasets to be generated, as required in emerging big data applications, e.g., social network analytics.

An efficient approach based on selective partitioning for maximal frequent itemsets mining

We present a maximal frequent itemset (MFI) mining algorithm based on selective partitioning called SelPMiner. It makes use of a novel data format named Itemset-count tree—a compact and optimized representation in the form of partition that reduces memory requirement. It also does selective partitioning of the database, which reduces runtime to scan database. As the algorithm progressively searches for longer frequent itemsets in a depth-first manner, it creates new partitions with even smaller sizes having less dimensions and unique data instances, which results in faster support counting. SelPMiner uses a number of optimizations to prune the search space. We also prove upper bounds on the amount of memory consumed by these partitions. Experimental comparisons of the SelPMiner algorithm with popular existing fastest MFI mining algorithms on different types of datasets show significant speedup in computation time for many cases. SelPMiner works especially well when the minimum support is low and consumes less memory.

Practical Privacy-Preserving Frequent Itemset Mining on Supermarket Transactions

Data mining is widely applied to establish connections among the items in massive datasets nowadays. Association rule mining is one of the most popular methods to perform data mining, and a fundamental part of this is frequent itemset mining. Big-scale data are uploaded to the honest-but-curious cloud service provider (CSP). Therefore, it is imperative to protect the raw data from being obtained by the CSP and the third parties. Furthermore, because supermarket transactions are sparse, they are not suitable to be mined using the same methods used for most of the other data. The methods used for ordinary data will cost more computation power if they are applied on this special dataset. In this paper, we propose an efficient protocol to evaluate whether an itemset is frequent or not under the encrypted mining query on supermarket transactions. To improve the mining efficiency, we design a blocking algorithm. In this algorithm, we separate the encrypted transactions into blocks and only calculate bilinear pairings on ciphertexts of part blocks instead of all ciphertexts, which helps cut down the computation cost of the mining process. Finally, we evaluate the performance of our protocol by conducting theoretical analyses and simulator experiments in the aspects of computation cost, security, correctness, and running time. The results demonstrate that our protocol can output a correct mining result and clearly outperforms the previous solution in the aspect of efficiency under the same security level.

Implementation of K-Means Algorithm using Clustering Rules on Medical Data Sets

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The K-Means algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or FP tree), and frequent item set is mining by using of FP tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (K-Means Algorithmn, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on spatial data to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on spatial data.

Analysis of Accuracy K-Means and Apriori Algorithms for Patient Data Clusters

The stacking data is usefull to get a new information. Data mining is a methode to determine the important pattern in Frequent Itemset Mining (FIM). Apriori is part of association rule that is used to determine the assosiative relationship in items combination. But apriori has a high computational time weakness because frequent itemset process searching must scan the database repeatedly for each itemset combination. This study aims to see the effect of the k-means clustering algorithm on the apriori algorithm by combining these two algorithms. The test results show that the combination of k-means and apriori algorithms produces more information detaily and faster time computating than the apriori algorithm with a total computing time of 21.93 minutes and a combination of k-means and apriori algorithms 17.41 minutes.

A Systematic Survey on High Utility Itemset Mining

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.