Frequent Itemset Mining Problem Research Articles

Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms.

On the Inverse Frequent Itemset Mining Problem for Condensed Representations of Itemsets

Inverse frequent itemset mining can be successfully modelled as an instance of the Probabilistic Satisfiability problem. Given a transaction database we can perform a frequent itemset mining algorithm, like the Apriori algorithm, to obtain useful itemset collections such as frequent or closed itemsets. We then use these itemset collections as frequency constraints in order to reconstruct the original database by solving a linear programming problem. There are cases however, where the reconstructed database is not in direct agreement with the original one. In this study, we analyse the degree of similarity between the original database and the reconstructed one, when different variations of condensed itemset representations are used as the initial frequency constraints. We examine how much the initial database properties and itemset relations have been preserved when we emphasize on the frequent itemset border and assess database quality by measuring database distance metrics. As this solution framework presents increased computational cost, we also consider a heuristic approach that is based on the notion that a transaction can be also considered as an itemset and compare the strengths and weaknesses of each framework when the same conditions apply. We manage to improve the efficiency of existing heuristic based approaches to the problem by utilizing smaller initial itemset collections. Our work contributes in (a) privacy preserving data mining and (b) gain in transaction database storage memory savings.

An integrated cuckoo search-genetic algorithm for mining frequent itemsets

Mining frequent itemsets is a contemporary research topic in the data-mining domain. Identifying frequent itemsets has acquired its significance due to recognized objectives in the field of association mining. The frequent itemset mining problem focuses on identifying frequent patterns and correspondence between the itemsets in the transactional database. Frequent itemset mining problems are time consuming when dealing with huge amounts of data and extracting the maximum number of the frequent itemsets from a large search space is a challenging process. Since the transactional database size is enormous, a stochastic method is applied to find optimal solutions. In this article, an integrated approach to extract frequent itemsets is proposed based on the cuckoo search and genetic algorithm. This integrated methodology is performed by merging the reproduction process of cuckoo species with the operators such as crossover and mutation in GA. First, a strategy to diversify the population and to discover maximum frequent patterns in the search process, Second, a neighborhood exploration strategy is applied for recurrent solutions to improve the accuracy. Finally, the proposed integrated approach is validated with respect to the solution quality and convergence rate. The integrated approach is verified using standard data instances. The ICSGA approach efficiently discovers the frequent patterns. The comparison results of the integrated approach are eminently productive than the other approaches in the literature.

Mining Closed High Utility Itemsets based on Propositional Satisfiability

A high utility itemset mining problem is the question of recognizing a set of items that have utility values greater than a given user utility threshold. This generalization of the classical problem of frequent itemset mining is a useful and well-known task in data analysis and data mining, since it is used in a wide range of real applications. In this paper, we first propose to use symbolic Artificial Intelligence for computing the set of all closed high utility itemsets from transaction databases. Our approach is based on reduction to enumeration problems of propositional satisfiability. Then, we enhance the efficiency of our SAT-based approach using the weighted clique cover problem. After that, in order to improve scalability, a decomposition technique is applied to derive smaller and independent sub-problems in order to capture all the closed high utility itemsets. Clearly, our SAT-based encoding can be constantly enhanced by integrating the last improvements in powerful SAT solvers and models enumeration algorithms. Finally, through empirical evaluations on different real-world datasets, we demonstrate that the proposed approach is very competitive with state-of-the-art specialized algorithms for high utility itemsets mining, while being sufficiently flexible to take into account additional constraints to finding closed high utility itemsets.

CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets

The problem of frequent itemset mining is one of the more important problems in data mining which has been extensively employed across a wide range of other relevant tasks such as market basket analysis in marketing, or text analysis in text mining applications. The majority of the deterministic frequent itemset mining algorithms which have been proposed in recent years use some sort or another of an optimal data structures to reduce the overall execution time of the algorithm. In this paper, however, we have tried instead to introduce an approximation algorithm which works by converting the problem into a clustering problem where similar transactions are grouped together. Each cluster centroid represents an itemset which may be assumed to be a candidate frequent itemsets. The validity of this assumption is simply verified by calculating the support count of these itemsets. Those who meet the min-support condition are considered to be an actual frequent itemset. As for the remaining itemsets, they are then passed to MAFIA which extract all maximal frequent itemsets therefrom. Experimentations made on several well-known and diverse datasets show that the proposed algorithm performs almost always faster, and in some cases up to 10 times faster, than the existing deterministic algorithms, and all this by retaining up to 95% of its accuracy.

Efficient Vertical Mining of High Average-Utility Itemsets Based on Novel Upper-Bounds

Mining High Average-Utility Itemsets (HAUIs) in a quantitative database is an extension of the traditional problem of frequent itemset mining, having several practical applications. Discovering HAUIs is more challenging than mining frequent itemsets using the traditional support model since the average-utilities of itemsets do not satisfy the downward-closure property. To design algorithms for mining HAUIs that reduce the search space of itemsets, prior studies have proposed various upper-bounds on the average-utilities of itemsets. However, these algorithms can generate a huge amount of unpromising HAUI candidates, which result in high memory consumption and long runtimes. To address this problem, this paper proposes four tight average-utility upper-bounds, based on a vertical database representation, and three efficient pruning strategies. Furthermore, a novel generic framework for comparing average-utility upper-bounds is presented. Based on these theoretical results, an efficient algorithm named dHAUIM is introduced for mining the complete set of HAUIs. dHAUIM represents the search space and quickly compute upper-bounds using a novel IDUL structure. Extensive experiments show that dHAUIM outperforms four state-of-the-art algorithms for mining HAUIs in terms of runtime on both real-life and synthetic databases. Moreover, results show that the proposed pruning strategies dramatically reduce the number of candidate HAUIs.

HighPU: a high privacy-utility approach to mining frequent itemset with differential privacy

In the field of data mining, frequent itemset mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however presents privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. And we use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.

HighPU: a high privacy-utility approach to mining frequent itemset with differential privacy

In the field of data mining, frequent itemset mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however presents privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. And we use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.

The MapReduce Model on Cascading Platform for Frequent Itemset Mining

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).

A new framework for metaheuristic-based frequent itemset mining

This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining (FIM), which considers intrinsic features of the FIM problem. The framework, called META-GD, can be used to steer any metaheuristics-based FIM approach. Without loss of generality, three metaheuristics are considered in this paper, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO). This allows to derive three approaches, named GA-GD, PSO-GD, and BSO-GD, respectively. An extensive experimental evaluation on medium and large database instances reveal that PSO-GD outperforms state-of-the-art metaheuristic-based approaches in terms of runtime and solution quality.

Finding Fuzzy Close Frequent Itemsets from Databases

In this paper, we define the problem of fuzzy close frequent itemset mining to discover the rules of the data. A concise tree-based data synoposis named FCTree is built, where the fuzzy itemsets are sorted by their supports. In addition, an algorithm called FCFIMiner is proposed to construct and maintain the FCTree. We conduct superset pruning from the result of the FCTree. The experimental works over 2 databases show the proposed algorithm has a much better performance.

Frequent Itemsets Mining for Big Data: A Comparative Analysis

Abstract Itemset mining is a well-known exploratory data mining technique used to discover interesting correlations hidden in a data collection. Since it supports different targeted analyses, it is profitably exploited in a wide range of different domains, ranging from network traffic data to medical records. With the increasing amount of generated data, different scalable algorithms have been developed, exploiting the advantages of distributed computing frameworks, such as Apache Hadoop and Spark. This paper reviews Hadoop- and Spark-based scalable algorithms addressing the frequent itemset mining problem in the Big Data domain through both theoretical and experimental comparative analyses. Since the itemset mining task is computationally expensive, its distribution and parallelization strategies heavily affect memory usage, load balancing, and communication costs. A detailed discussion of the algorithmic choices of the distributed methods for frequent itemset mining is followed by an experimental analysis comparing the performance of state-of-the-art distributed implementations on both synthetic and real datasets. The strengths and weaknesses of the algorithms are thoroughly discussed with respect to the dataset features (e.g., data distribution, average transaction length, number of records), and specific parameter settings. Finally, based on theoretical and experimental analyses, open research directions for the parallelization of the itemset mining problem are presented.

Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem

Exact approaches to Frequent Itemsets Mining (FIM) are characterised by poor runtime performance when dealing with large database instances. Several FIM bio-inspired approaches have been proposed to overcome this issue. These are considerably more efficient from the point of view of runtime performance, but they still yield poor quality solutions. The quality of the solution, i.e., the number of frequent itemsets discovered, can be increased by improving the randomised search of the solutions space considering intrinsic features of the FIM problem. This paper proposes a new framework for FIM bio-inspired approaches that considers the recursive property of frequent itemsets, i.e., the same feature exploited by the Apriori exact heuristic, in the search of the solution space. We define two new approaches to FIM, namely GA-Apriori and PSO-Apriori, based on the proposed framework, which use genetic algorithms and particle swarm optimisation, respectively. Extensive experiments on synthetic and real database instances show that the proposed approaches outperform other bio-inspired ones in terms of runtime performance. The results also reveal that the performance of PSO-Apriori is comparable to the one of exact approaches Apriori and FPGrowth in respect of the quality of solutions found. We also show that PSO-Apriori outperforms the recently developed BATFIM algorithm when dealing with very large database instances.

A survey of itemset mining

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

A formal series-based unification of the frequent itemset mining approaches

Over the last two decades, a great deal of work has been devoted to the algorithmic aspects of the frequent itemset (FI) mining problem, leading to a phenomenal number of algorithms and associated implementations, each of which claims supremacy. Meanwhile, it is generally well agreed that developing a unifying theory is one of the most important issues in data mining research. Hence, our primary motivation for this work is to introduce a high-level formalism for this basic problem, which induces a unified vision of the algorithmic approaches presented so far. The key distinctive feature of the introduced model is that it combines, in one fashion, both the qualitative and the quantitative aspects of this basic problem. In this paper, we propose a new model for the FI-mining task based on formal series. In fact, we encode the itemsets as words over a sorted alphabet and express this problem by a formal series over the counting semiring $$(\mathbb N,+,\times ,0,1)$$(N,+,×,0,1), whose range represents the itemsets, and the coefficients are their supports. The aim is threefold: First, to define a clear, unified and extensible theoretical framework through which we can state the main FI-approaches. Second, to prove a convenient connection between the determinization of the acyclic weighted automaton that represents a transaction dataset and the computation of the associated collection of FI. Finally, to devise a first algorithmic transcription, baptized Wafi, of our model by means of weighted automata, which we evaluate against representative leading algorithms. The obtained results show the suitability of our formalism.

Data placement in massively distributed environments for fast parallel mining of frequent itemsets

Frequent itemset mining presents one of the fundamental building blocks in data mining. However, despite the crucial recent advances that have been made in data mining literature, few of both standard and improved solutions scale. This is particularly the case when (1) the quantity of data tends to be very large and/or (2) the minimum support is very low. In this paper, we address the problem of parallel frequent itemset mining (PFIM) in very large databases and study the impact and effectiveness of using specific data placement strategies in a massively distributed environment. By offering a clever data placement and an optimal organization of the extraction algorithms, we show that the arrangement of both the data and the different processes can make the global job either completely inoperative or very effective. In this setting, we propose two different highly scalable, PFIM algorithms, namely P2S (parallel-2-steps) and PATD (parallel absolute top-down). P2S algorithm allows discovering itemsets from large databases in two simple, yet efficient parallel jobs, while PATD renders the mining process of very large databases more simple and compact. Its mining process is made up of only one parallel job, which dramatically reduces the running time, the communication cost and the energy power consumption overhead in a distributed computational platform. Our different proposed approaches have been extensively evaluated on massive real-world data sets. The experimental results confirm the effectiveness and scalability of our proposals by the important scale-up obtained with very low minimum supports compared to other alternatives.

PFIN: A Parallel Frequent Itemset Mining Algorithm Using Nodesets

Frequent Itemset Mining (FIM) is one of most fundamental techniques in data mining with extensive applications to a variety of data mining problems such as association rule mining, correlations, clustering and classification. Since the first proposal of frequent itemset mining, numerous serial algorithms have been proposed in order to improve mining performance, yet most of them cannot scale to massive datasets which are very common nowadays. In this paper, we propose a new parallel FIM algorithm named PFIN based on Nodeset which is a more efficient data structure for mining frequent itemsets. PFIN can intelligently decompose a large-scale FIM problem into a set of tasks, where each task can be executed in parallel without unnecessary communication overheads. Moreover, a hash-based load balancing strategy has been adopted to optimize resource use and maximize throughput. For evaluating the performance of PFIN, we have conduct extensive experiments on Spark which is an emerging distributed in-memory processing framework to compare it against PFP which is one of state-of-the-art parallel FIM algorithms on a range of real datasets. The experimental results demonstrate that our proposed PFIN are highly competitive with PFP in scalability performance, outperforming PFP in speed performance.

Mining Frequent Itemsets in Correlated Uncertain Databases

Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, e.g., RFID data, sensor data, real-time video data, has been collected. As one of the most fundamental issues of uncertain data mining, uncertain frequent pattern mining has attracted much attention in database and data mining communities. Although there have been some solutions for uncertain frequent pattern mining, most of them assume that the data is independent, which is not true in most real-world scenarios. Therefore, current methods that are based on the independent assumption may generate inaccurate results for correlated uncertain data. In this paper, we focus on the problem of mining frequent itemsets over correlated uncertain data, where correlation can exist in any pair of uncertain data objects (transactions). We propose a novel probabilistic model, called Correlated Frequent Probability model (CFP model) to represent the probability distribution of support in a given correlated uncertain dataset. Based on the distribution of support derived from the CFP model, we observe that some probabilistic frequent itemsets are only frequent in several transactions with high positive correlation. In particular, the itemsets, which are global probabilistic frequent, have more significance in eliminating the influence of the existing noise and correlation in data. In order to reduce redundant frequent itemsets, we further propose a new type of patterns, called global probabilistic frequent itemsets, to identify itemsets that are always frequent in each group of transactions if the whole correlated uncertain database is divided into disjoint groups based on their correlation. To speed up the mining process, we also design a dynamic programming solution, as well as two pruning and bounding techniques. Extensive experiments on both real and synthetic datasets verify the effectiveness and efficiency of the proposed model and algorithms.

Pruning strategies for mining high utility itemsets

High utility itemset mining problem involves the use of internal and external utilities of items (such as profits, margins) to discover interesting patterns from a given transactional database. It is an extension of the basic frequent itemset mining problem and is proven to be considerably hard and intractable. This is due to the lack of inherent structural properties of high utility itemsets that can be exploited. Several heuristic methods have been suggested in the literature to limit the large search space. This paper aims to improve the state-of-the-art and proposes a high utility mining method that employs novel pruning strategies. The utility of the proposed method is demonstrated through rigorous experimentation on several real and synthetic benchmark sparse and dense datasets. A comparative evaluation of the method against a state-of-the-art method is also presented. Our experimental results reveal that the proposed method is very effective in pruning unpromising candidates, especially for sparse transactional databases.

An efficient method for mining frequent itemsets with double constraints

Constraint-based frequent itemset mining is necessary when the needs and interests of users are the top priority. In this task, two opposite types of constraint are studied, namely anti-monotone and monotone constraints. Previous approaches have mainly mined frequent itemsets that satisfy one of these two types of constraint. Mining frequent itemsets that satisfy both types is of interest. The present study considers the problem of mining frequent itemsets with the following two conditions: they include a set C0 (monotone) and contain no items of set C′1 (anti-monotone), where the intersection of C0 and C′1 is empty and they are changed regularly. A unique representation of frequent itemsets restricted on C0 and C′1 using closed itemsets and their generators is proposed. Then, an algorithm called MFS_DoubleCons is developed to quickly and distinctly generate all frequent itemsets that satisfy the constraints from the lattice of closed itemsets and generators instead of mining them directly from the database. The theoretical results are proven to be reliable. Extensive experiments on a broad range of synthetic and real databases that compare MFS_DoubleCons to dEclat-DC (a modified version of dEclat utilized to mine frequent itemsets with constraints) show the effectiveness of our approach.