Frequent Itemset Mining Research Articles

DisCANTree: A Distributed Algorithm for Incremental Frequent Itemset Mining based on MapReduce

Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model MapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.

Directions of membrane separator development for microbial fuel cells: A retrospective analysis using frequent itemset mining and descriptive statistical approach

To increase the efficiency of microbial fuel cells (MFCs), the separator (which is mostly a membrane) placed between the electrodes or their compartments is considered of high importance besides several other biotic and abiotic factors (e.g. configuration, mode of operation, types of inoculum and substrate). Nafion-based proton exchange membranes (PEMs) are the most widespread, although these materials are often criticized on various technological and economical grounds. Therefore, to find alternatives of Nafion, the synthesis, development and testing of novel/commercialized membrane separators with enhanced characteristics have been hot topics. In this study, the goals were to assess the membrane-installed MFCs in a retrospective manner and reveal the trends, the applied practices, frequent setups, etc. via Bayesian classification and frequent itemset mining algorithms. Thereafter, a separate discussion was devoted to examine the current standing of research related to major membrane groups used in MFCs and evaluate in accordance with the big picture how the various systems behave in comparison with each other, especially compared to those applying Nafion PEM. It was concluded that some membrane types seem to be competitive to Nafion, however, the standardization of the experiments would drive the more unambiguous comparison of studies.

Distributed Frequent Itemset Mining Using Size Based Assignment Technique

Distributed Frequent Itemset Mining Using Size Based Assignment Technique

SAT‐based and CP‐based declarative approaches for Top‐Rank‐ K closed frequent itemset mining

SAT‐based and CP‐based declarative approaches for Top‐Rank‐ <i>K</i> closed frequent itemset mining

A Survey of High-utility Itemsets Mining

Data mining is of significance for finding useful information in massive data. Frequent itemsets mining (FIM ) and high-utility itemsets mining(HUIM) are extremely common and wide application in research and real life. For one thing, HUIM algorithm focuses on utility, which is more practical. It can be used to find high profit goods, items with user’s preference, etc. For another, the difference between utility and frequency determines that HUIM and FIM algorithms are different. In order to introduce HUIM algorithms in the round, this paper showed typical HUIM algorithms for static data and stream data separately in section 2 and section 3. Meanwhile, section 2 partitioned algorithms based on candidates generation and threshold. Section 3 showed algorithms in terms of window model which is necessary to stream data mining. Lastly, this paper made a conclusion of referred HUIM algorithms and proposed some research prospects for this work.

Enriching E Commerce Fraud Detection by using Machine Learning

As there has been a proliferation of the internet platform, it has been increasingly getting affordable for a lot of individuals. The rise has been instrumental in achieving several services including the E-commerce platform. This has led to an unprecedented increase in the amount of fraud that is being committed on this platform. The fraud that is being committed on the E-commerce platforms is very different from the frauds committed on other platforms online. Numerous researches have been performed to combat the evils of credit card frauds and money laundering rings. But there is a severe lack of research on the fraud that is committed on the E-commerce platform. Therefore, this research paper defines an innovative approach for the identification of fraud on E-commerce platforms through the implementation of machine learning approaches. The presented technique utilizes Linear Clustering, Entropy Estimation and Frequent itemset mining in addition to the inclusion of Artificial Neural Networks, Hypergraph formation and Fuzzy classification. The implementation of this system will give more security for E-commerce platform-based transactions by identifying fraudulent activities with better efficiency. The methodology has been tested extensively through rigorous experimentation to evaluate the performance metrics which yielded significantly positive results.

Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth

Nowadays the Frequentitemset mining (FIM) is an essential task for retrieving frequently occurring patterns, correlation, events or association in a transactional database. Understanding of such frequent patterns helps to take substantial decisions in decisive situations. Multiple algorithms are proposed for finding such patterns, however the time and space complexity of these algorithms rapidly increases with number of items in a dataset. So it is necessary to analyze the efficiency of these algorithms by using different datasets. The aim of this paper is to evaluate theperformance of frequent itemset mining algorithms, Apriori and Frequent Pattern (FP) growth by comparing their features. This study shows that the FP-growth algorithm is more efficient than the Apriori algorithm for generating rules and frequent pattern mining.

Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy

The problem of mining frequent weighted itemsets (FWIs) is an extension of the mining frequent itemsets (FIs), which considers not only the frequent occurrence of items but also their relative importance in a dataset. However, like mining FIs, mining FWIs usually produces a large result set, which makes it difficult to extract rules and creates redundancy. The problem of mining frequent weighted closed itemsets (FWCIs) has been proposed as a solution to this issue, which produces a smaller result set while preserving sufficient information to extract rules. The weighted node-list (WN-list) structure is currently considered the state-of-the-art structure for mining FWIs. In this study, we first propose the definition of WN-list ancestral operation and a theorem as the theoretical basis for eliminating unsatisfactory candidates, then propose an efficient algorithm, namely NFWCI, for mining FWCIs using the WN-list and an early pruning strategy. The experimental results on many sparse and dense datasets show that the proposed algorithm outperforms the-state-of-the-art algorithm for mining FWCIs.

A Dynamic Sliding Window based Balanced Parallel Frequent Itemset Mining Algorithm in Data Stream

A Dynamic Sliding Window based Balanced Parallel Frequent Itemset Mining Algorithm in Data Stream

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

Objectives: To achieve improved performance of FP-Growth based Association Rule Mining algorithm for massive data by effective utilization of storage,execution capability and improved partition technique within the Hadoop MapReduce framework. Methodology: The proposed methodology has four main phases: In the first phase, the item sets for finding the frequent pattern are encoded and thus minimizes the expensive operation for large data set. In the second phase, improved hash partitioning reduces the network overhead and improves the communication speed within the MapReduce phase for each item set. The effective usage of network bandwidth and storage is obtained by the impact of compression in the third phase. The use of combiner in final phase for frequent item set mining minimizes the overhead of reduce phase by finding the pattern in each partition and minimizes the overall execution time of the FP-Growth algorithm. Findings: FP-Growth based association rule mining algorithm is designed for parallel execution on distributed cluster of servers. Changes to the MapReduce implementation of FP-Growth with the impact of encoding. Improved hash partitioning, compression and configuration results in a significant performance gain with better improvement in execution time.Novelty/Improvements: According to the experimental results, the changes in storage and processing level within the MapReduce framework improves the overall performance of the parallel frequent item set mining in Hadoop cluster. Keywords: Association rule mining; Hadoop; MapReduce; FP-Growth

Discovering informative features in large-scale landmark image collection

One of the key problems in image retrieval systems is the presence of irrelevant and noisy image content. Such content can cause significant confusion for the system. Therefore, there is a need to represent images with only informative features in order to improve the retrieval performance of the system or any subsequent process. In this article, we propose a method to identify the informative features in a large-scale image collection. We apply the frequent itemset mining (FIM) approach to extract visual features patterns from a list of images of the same object. Then, we generate feature pairs to measure the significance of each feature depending on the co-occurrence with its neighbouring features. In addition, we apply this feature selection technique to localise the landmark in the image. The performance of the proposed method is evaluated in terms of average precision (AP) on two benchmark data sets and found that it gives a comparable retrieval performance over the bag of visual words baseline system and the previous methods.

Application of MapReduce parallel association mining on IDS in cloud computing environment

The rise of the cloud computing model has resulted in more than terabytes of data being stored in the cloud platform every day on the Internet. Mining valuable information from these massive data has become an emerging industry direction, but the current Intrusion-detection system (IDS) has been unable to adapt to large-scale log information mining. Therefore, an association rule mining algorithm based on MapReduce parallel computing framework is proposed. Firstly, the frequent itemsets mining algorithm Apriori is analyzed, and the MapReduce model is used to parallelize and improve it to more efficiently complete the mining of frequent itemsets. Secondly, the parallel Apriori is designed to run on IDS. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. The results show that the proposed method has higher detection efficiency when processing massive data, and requires less processing time.

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.

Incremental frequent itemsets mining based on frequent pattern tree and multi-scale

Multi-scale can reveal the structure and hierarchical characteristics of the data objects to reflect their essence from different perspectives and levels. An incremental frequent itemsets mining algorithm based on frequent pattern tree is proposed by incorporating multi-scale theory(simplified to FP-tree and Multi-Scale based Incremental Mining, FPMSIM). FPMSIM uses the classic FP-Growth to construct a pattern tree and generate frequent itemsets for more fine-grained dataset which is called benchmark scale dataset. The newly added dataset is also independently mined as a benchmark scale dataset. The ultimate frequent itemsets for the target scales are derived by means of the scale-up process. In which, some unknown itemsets counts need to be estimated by comparing the similarity among benchmark scale datasets. In this way, severe dataset rescanning and tree structure adjustment overhead are avoided during the maintenance process. The experimental results show that although the support estimation error will lead to incomplete frequent itemsets mining, it can be offset by the performance gains in the mining efficiency and I/O cost, especially in the field of big data.

An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix

High utility itemset mining is an important extension of frequent itemset mining which considers unit profits and quantities of items as external and internal utilities, respectively. Since the utility function has not downward closure property, an overestimated value of utility is obtained using an anti-monotonic upper bound of utility function to prune the search space and improve the efficiency of high utility itemset mining methods. Transaction-weighted utilization (TWU) of itemset was the first and one of the most important functions which has been used as the anti-monotonic upper bound of utility by various algorithms. A variety of high utility itemset mining methods have attempted to tighten the utility upper bound and have exploited appropriate pruning strategies to improve mining efficiency. Although TWU and its improved alternatives have attempted to increase the efficiency of high utility itemset mining methods by pruning their search spaces, they suffer from a significant number of generated candidates which are high-TWU but are not high utility itemsets. Calculating the actual utilities of low utility candidates needs to multiple scanning of the dataset and thus imposes a huge overhead to the mining methods, which can cause to lose the pruning benefits of the upper bounds. Proposing appropriate pruning strategies, exploiting efficient data structures, and using tight anti-monotonic upper bounds can overcome this problem and lead to significant performance improvement in high utility itemset mining methods. In this paper, a new projection-based method, called MAHI (matrix-aided high utility itemset mining), is introduced which uses a novel utility matrix-based pruning strategy, called MA-prune to improve the high utility itemset mining performance in terms of execution time. The experimental results show that MAHI is faster than former algorithms.

A new approximate method for mining frequent itemsets from big data

Mining frequent itemsets in transaction databases is an important task in many applications. It becomes more challenging when dealing with a large transaction database because traditional algorithms are not scalable due to the limited main memory. In this paper, we propose a new approach for the approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it uses the frequent itemsets from a subset of the entire database to approximate the result of the whole data, and can be implemented in a distributed environment. Our algorithm is able to efficiently produce high-accurate results, however it misses some true frequent itemsets. To address this problem and reduce the number of false negative frequent itemsets we introduce an additional parameter to the algorithm to discover most of the frequent itemsets contained in the entire data set. In this article, we show an empirical evaluation of the results of the proposed approach.

Anytime Frequent Itemset Mining of Transactional Data Streams

Mining frequent itemsets from transactional data streams has become very essential in today's world with many applications such as stock market analysis, retail chain analysis, web log analysis, etc. Various algorithms have been proposed to efficiently mine single-port and multi-port transactional streams within the constraints of limited time and memory. However, all of them are budget algorithms, i.e., they are not capable of handling varying inter-arrival rate of transactions and high speed streams. They are constrained by a maximum limit to the inter-arrival rate of transactions, beyond which they fail to process. Also, these algorithms are not capable of giving immediate mining results, even with compromised accuracy if required. The above two properties characterize an anytime algorithm. We propose AnyFI, which is the first anytime frequent itemset mining algorithm for data streams. AnyFI uses a novel data structure - BFI-forest, which is capable of handling transactions arriving at variable rate. It maintains itemsets in BFI-forest in such a way that it can give a mining result almost immediately when the time allowance to mine is very less and can refine its accuracy with increase in time allowance. We also propose MPAnyFI which extends AnyFI into a parallel framework for anytime frequent itemset mining of multi-port data streams over commodity clusters. It uses AnyFI at each computing node of the cluster. Our extensive experimental analysis shows that AnyFI can handle high stream speeds close to 60,000 trans/sec with recall close to 100%. They also show the efficiency of MPAnyFI.

Mining frequent itemsets from streaming transaction data using genetic algorithms

This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using genetic algorithms is presented, and various relationships between concept drift, sliding window size, and genetic algorithm constraints are explored. Concept drift is identified by changes in frequent itemsets. The novelty of this work lies in determining concept drift using frequent itemsets for mining streaming data, using the genetic algorithm framework. Formulas have been presented for calculating minimum support counts in streaming data using sliding windows. Testing highlighted that the ratio of the window size to transactions per drift was a key to good performance. Getting good results when the sliding window size was too small was a challenge since normal fluctuations in the data could appear to be a concept drift. Window size must be managed in conjunction with support and confidence values in order to achieve reasonable results. This method of detecting concept drift performed well when larger window sizes were used.

Toward fault-tolerant and secure frequent itemset mining outsourcing in hybrid cloud environment

Due to the rising costs of maintaining IT infrastructures for large-scale data mining, it is becoming a trend for data owners to outsource data mining tasks together with storage to cloud service providers, however, which also arouses security concerns on unauthorized breaches of data confidentiality and result integrity. Existing solutions yet seldom protect data privacy whilst guaranteeing result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks by employing Shamir’s secret sharing scheme. Based on those subprotocols, an efficient frequent itemset mining protocol is designed under hybrid cloud setting, in which the public unreliable cloud and semi-trusted cloud cooperate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attack, but also verifies the integrity of mining results. Theoretical analysis demonstrates that the scheme ensures security as well as fault tolerance under our threat model. Experimental evaluations show that our proposed protocol outperforms the similar solution regarding efficiency while it can detect and correct cloud servers’ errors effectively.

Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity and scale of the services running across global data centers. It is often difficult to review the logs jointly for understanding production issues given the distributed nature of the system. Additionally, there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling largescale production datasets. We have successfully rolled out this approach for root cause investigation purposes within Facebook's infrastructure. We also present the setup and results from multiple production use cases in this paper.