Algorithm For Frequent Itemsets Research Articles

A Node sets based Fast and Scalable Frequent Itemset (FSFIM) Algorithm for Mining Big Data using MapReduce Paradigm

A Node sets based Fast and Scalable Frequent Itemset (FSFIM) Algorithm for Mining Big Data using MapReduce Paradigm

A node sets based fast and scalable frequent itemset algorithm for mining big data using map reduce paradigm

A node sets based fast and scalable frequent itemset algorithm for mining big data using map reduce paradigm

Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control

Business success is a business that is able to compete and grow keep abreast of developments in the business world. Especially in the retail sector, where competition is getting tighter. Business owners need to pay attention to the layout of goods and stock management to improve service and meet consumer needs because consumers often have difficulty in finding goods. On the other hand, shortages and excess stock often occur due to lack of goods management. Based on these problems, appropriate techniques are needed for the management of goods supply, one of which is to apply techniques found in the branch of science. Data mining is a technique of association rules. This study aims to find patterns of placement and purchase of goods in generating Association Rule using FP-Growth algorithm. The dataset in this study used data on sales of goods in clothing stores. The results of the study of 140 transactions there are 24 association rules consisting of 7 association rules with 2-itemsets and 17 association rules with 3-itemsets that most often appear in transactions. Based on the order of the highest support value, namely CKJ→STX^LK with a support value of 67%, while the highest confidence value, there are 3 association rules that get the same value, namely STX^CKJ→LK, STX^CAK→LK, STX^RI→LK with a value of 100%. Thus, the rules of association produced by the frequent itemset algorithm, FP-growth, can serve as decision support for the sales of goods in small and medium-sized retail businesses

Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses.

A Survey on Automated Support Threshold Based on Apriori Algorithm for Frequent Item sets

Data mining is the technique to extract features from raw data. In Today’s era data mining has a lot of e- Commerce applications. It is widely used in a variety of application areas like banking, marketing and retail industry. The association rules generated from them are still important items. Apriori based algorithms tend to achieve high efficiency; when the database transactions are scarce.Study proposes an approach to deal with frequent item problem. Main goal is to provide an algorithm for frequent itemset mining with automated support thresholds. Apriori follows breadth search and bottom up approaches. It enumerates all frequent items with some modifications. It only checks the items only when it is existed in database for making more frequent itemset. It reduces the time complexity as well as space complexity with the more frequent outcome of itemsets. Key Words: Data Mining, Apriori based Algorithm

A Survey on Automated Support Threshold Based on Apriori Algorithm for Frequent Item sets

The "Educational System for College" is an advanced educational management platform designed to streamline administrative tasks and enhance the learning experience. Key components include secure user authentication, cloud hosting, a MEAN stack-based dashboard for attendance and record management, facial recognition for student identification, and DynamoDB integration for data storage. The project’s objectives are to improve student data management, automate attendance tracking, simplify grade distribution, enhance communication, and centralize placement information. It represents a significant step towards modernizing college education and promoting student success. Keywords:- Educational System for College, Administrative System, Enhanced Learning Experiences, User Authentication, User-Friendly process, Data Privacy, etc.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism

The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.

An efficient biobjective evolutionary algorithm for mining frequent and high utility itemsets

Mining frequent and high utility itemsets (FHUIs) from transactional databases is essential in data mining. From a multiobjective perspective, modelling the task of mining FHUIs in a unified framework requires support and utility to be considered simultaneously. In contrast to traditional algorithms for mining FHUIs, multiobjective evolutionary algorithms (MOEAs) can overcome the difficulty of setting the parameter and can generate multiple solutions in one pass, which brings advantages to mining FHUIs. However, MOEAs may be inefficient when the number of transactions and the number of items in the transaction database are large. To address this problem, we propose an efficient biobjective evolutionary algorithm for obtaining FHUIs (BOEA-FHUI) based on three novel strategies. In BOEA-FHUI, a pruning strategy is proposed to reduce the search space. Based on the pruning results, a repair strategy is proposed to make the generated inferior offspring jump out of the dominated region of the previous Pareto solutions. With the proposed pruning and repair strategies, the search space can be significantly reduced, which helps improve the search efficiency. To increase the number of items with higher support and higher utility values, an improved mutation strategy based on the sparse nature of the FHUI is proposed, which can accelerate the convergence speed of the algorithm. The experimental results on the real-world and synthetic datasets show that the proposed algorithm performs better than state-of-the-art MOEAs in finding FHUIs.

CIP- Efficient Method for Mining Frequent Itemsets from Data streams using Landmark Window Model

Continuous stream transactions like network monitoring, retail market data analysis and stock market prediction need the “frequent patterns” to be detected recurrently. Literature suggests that several pattern mining solutions are being developed over years. Still lot of challenges need to be addressed due to rapidness in generation of continuous, unbounded and ordered data real time. Hence extraction of frequent patterns from recent data will improve the analysis of stream data. In this article, a new landmark window model CIP (candidate indexing and pruning) is considered for mining the datasets. CIP allows us to mine over entire history of data streams, which improves the accuracy. This article also proposes the candidate indexed sub (CIS)-tree scheme to extract the essential information from each incoming transactions of data streams. Our proposal is compared with the existing “improved data stream mining” (ISDM) for maximal frequent itemsets algorithm. Extensive experimental analyses prove the superiority of the proposed CIP over the popular ISDM in terms of accuracy and time complexity for high-speed data stream. This article also covers up a case study where the proposed approach is applied for an application called “web prefetching”.

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

A Recursive Algorithm for Mining Association Rules

Mining frequent itemsets and association rules are an essential task within data mining and data analysis. In this paper, we introduce PrefRec, a recursive algorithm for finding frequent itemsets and association rules. Its main advantage is its recursiveness with respect to the items. It is particularly efficient for updating the mining process when new items are added to the database or when some are excluded. We present in a complete way the logic of the algorithm, and give some of its applications. After that, we carry out an experimental study on the effectiveness of PrefRec. We first compare the execution times with some very popular frequent itemset mining algorithms. Then, we do experiments to test the updating capabilities of our algorithm.

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

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.

Research on Detection and Diagnosis Technology of Subarachnoid Hemorrhage Based on New Association Classification Algorithm

Subarachnoid hemorrhage (SAH) is one of the most common cerebrovascular emergencies, which can lead to serious consequences. Spontaneous subarachnoid hemorrhage accounts for about 15% of acute cerebrovascular accidents. Among them, spontaneous subarachnoid hemorrhage caused by rupture of an intracranial aneurysm or vascular malformation is more common, accounting for about 85%. Therefore, it is very important to detect the early symptoms of subarachnoid hemorrhage through reasonable means and to carry out appropriate clinical intervention and treatment. With the development of imaging technology, computed tomography angiography (CTA) is widely used in clinical practice. However, the accuracy of manual recognition of CT images is not very high, and the efficiency is low. The emergence of data mining technology is gradually solving this problem. In this paper, we introduce and summarize the development of data mining, domestic and foreign research progress, the application status of data mining in the medical field, and the main technologies and methods of data mining. We study the application of association rule extraction technology in data mining in the medical field. The Apriori algorithm for finding frequent item sets in association rule extraction and its series of improved algorithms are studied and finally combined the characteristics of medical CT images, an image mining method of association rules based on the gray-level cooccurrence matrix is proposed. Based on the FP-growth algorithm, the NCFP-growth algorithm based on association rules is proposed and compared with the mining effect of several other algorithms. The proposed algorithm achieves a classification accuracy of above 90%, which is higher than the Apriori algorithm and its improved variations.

Matching Network of Ontologies: A Random Walk and Frequent Itemsets Approach

A System of Systems (SoS) is a complex set of IS (Information Systems) created by the aggregation and interconnection of ISs. SoS brings unexpected behavior and functionality during its construction to the benefit of its users and the SoS itself. The integration of SoSs is a matter of time. However, as SoSs can behave as an organization, it may be inappropriate to try to integrate individual IS members separately. On the other hand, manually integrating the SoS as a whole can be unfeasible due to its complexity. If an SoS has ontologies modeling the knowledge, the integration of SoSs can be translated into a problem of a network of ontologies alignment. However, it creates another challenge: computing each possible pair of entities inside each network’s ontology can have unfeasible execution time, even using the best matchers available. In this article, we propose to mine the data from the networks using random walks and frequent item sets algorithm and discover relevant nodes elected as candidate entities. Next, the networks are pruned by an algebraic method eliminating identical entities. The relevant nodes are reinserted in the network to avoid losing essential correspondences. After the pre-processing step, data is sent to two matchers to obtain metrics and compare the results with the pairwise brute force approach and previous work. We identified relevant nodes with recall up to 0.75. The results are promising since precision and recall are closer to the force brute, and execution time is shorter, even more, when the size of the networks and the number of ontologies to be compared increases. We validate our approach using ontologies created from the OAEI (Ontology Alignment Evaluation Initiative).

A Survey on the Online Shopping Customer Review Data using Association Rule Mining

Revealing complex associations between entities is of vast significance for business optimization, prediction and decision making. Such associations include not only co-occurrence-based explicit relations but also non co-occurrence-based implicit ones. Associative rule mining (ARM) is used to study these implicit and explicit relationships. Online shopping customer review (OSCR) data has become a major information resource for consumers and has extremely important implications for a wide range of management activities. Consumer reviews examine the bond between service quality and customer purchase behaviour in online shopping context. Apriori is a key algorithm for mining frequent item sets for Boolean association rules. To develop the efficiency of the level-wise generation of frequent itemsets in online customer shopping customer review data, Apriori property is used to reduce the search space .The detection of interesting patterns in this collection of data can guide to important marketing and management strategic decisions. In this survey paper, some of the research work carried out on customer online shopping data is discussed. Also, the use of Apriori algorithm for the same type of data set is analyzed.

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.

Association Rule Mining on Spambase Dataset using Tanagra

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.

Efficient weighted probabilistic frequent itemset mining in uncertain databases

AbstractUncertain data mining has attracted so much interest in many emerging applications over the past decade. An issue of particular interest is to discover the frequent itemsets in uncertain databases. As an item would not appear in a transaction of such database for certain, several probability models are presented to measure the frequency of an itemset, and the frequent itemset over probabilistic data generally has two different definitions: the expected support‐based frequent itemset and probabilistic frequent itemset. Meanwhile, it is noted that the frequency itself cannot identify useful or meaningful patterns in some scenarios. Other measures such as the importance of items should be also taken into account. To this end, some studies recently have been done on weighted (importance) frequent itemset mining in uncertain databases. However, they are only designed for the expected support‐based frequent itemset, and suffer from low efficiency due to generating too many frequent itemset candidates. To address this issue, we propose a novel weighted probabilistic frequent itemsets (w‐PFIs) algorithm. Moreover, we derive a probability model for the support of a w‐PFI candidate in our method and present three pruning techniques to narrow the search space and remove the unpromising candidates immediately. Extensive experiments have been conducted on both real and synthetic datasets, to evaluate the performance of our w‐PFI algorithm in terms of runtime, accuracy and scalability. Results show that our algorithm yields the best performance among the existing algorithms.

SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming

Finding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data, it is necessary to develop a distributed frequent itemset mining algorithm to meet the needs of massive streaming data processing. Apache Spark is a unified analytic engine for massive data processing which has been successfully used in many data mining fields. In this paper, we propose a distributed algorithm for mining frequent itemsets over massive streaming data named SWEclat. The algorithm uses sliding window to process streaming data and uses vertical data structure to store the dataset in the sliding window. This algorithm is implemented by Apache Spark and uses Spark RDD to store streaming data and dataset in vertical data format, so as to divide these RDDs into partitions for distributed processing. Experimental results show that SWEclat algorithm has good acceleration, parallel scalability and load balancing.