Generate Association Rules Research Articles

A real-time data association of internet of things based for expert weather station system

The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.

A Novel Data Mining Algorithm and Its Applications in Basketball Match Technique and Tactical Analysis

The basketball game is a process in which players use different basic basketball methods to change their actions according to certain tactical structural forms. In the field of basketball, a huge data is generated during training, matches or competitions, sports management, and national physical fitness tests. During basketball matches, the management uses numerous methods to collect data about opponent teams, some of which are intuitive, while others may not be able to directly display their important information. In the sports domain, the coaches and managers use data mining techniques for transforming sports data into actionable knowledge and training their athletes for possible predictions of the outcomes of the games. This article focuses on the analysis application of data acquisition and preprocessing in basketball techniques, and tactical analysis is studied using the proposed data mining algorithm. The proposed algorithm and data of basketball games were used to make association rules analysis to analyze technical and tactical characteristics. The algorithm generates association rules based on the frequent item sets of basketball technical moves. It is evident from the experimental results that the proposed algorithm leads to high accuracy and better outcomes in terms of prediction.

Association Rule Generation for Student Performance Analysis usingApriori Algorithm

Association Rule Generation for Student Performance Analysis usingApriori Algorithm

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm.

The Apriori algorithm is used to conduct an in-depth analysis and research on the relationship between data mining and penalty decision of multiattribute data in the basketball game scene. The technical and tactical features are analyzed using an improved Apriori algorithm for association rule analysis of basketball game data. The algorithm generates association rules based on mining the set of frequent items among basketball technical actions. The improved algorithm can mine the technical moves that are more connected in the game data, and the analysis results are highly instructive. The technical and tactical directed analysis is divided into two parts: technical and tactical directed action analysis and technical and tactical directed cooperation analysis. The key action analysis uses Markov process-based data mining algorithm to analyze the basketball game data for key score transfer steps and key score loss transfer steps. The algorithm can find the key actions of scoring and key actions of conceding points in the game process, and the analysis results can guide basketball training and games, which has high practical value. Using the collated game data as the independent variable and the number of games won and lost as the dependent variable, logistic regression analysis is applied to derive the characteristics that affect winning. Again, the decision tree algorithm is used to select the significant features that affect winning and to make predictions of team performance. Finally, the technical statistics of the main players in the last three seasons are selected, and the association rule algorithm is applied to derive the degree of influence of player performance on the outcome of the game.

An Enhanced Fast – High Utility Item set Mining Method for Large Datasets

High Utility Itemset mining is considered one of the critical and challenging problems in data mining. The existing mining framework is limited to analyzing occurrence counts of items in the Database. However, this framework applies a single minimum utility threshold value that fails to consider different item characteristics. Recent methods of association mining focused on finding the high utility itemsets instead of frequent itemsets generations. Some utility-based mining methods that is Faster High Utility Itemset Mining (FHM), High Utility Itemset Miner (HUI-Miner), Direct Discovery of High Utility Patterns (D2HUP), Utility Pattern Growth (UP Growth & UP Growth+) are studied for the generation of high utility itemsets generations. Existing HUI mining methods are effectively generating HUIs. However, developing a faster and memory-efficient HUI mining method is required. For this purpose, this work develops an Enhanced Fast - High Utility Itemset Mining (EF-HUIM) method for the faster generation of high utility itemsets and respective association rules.

Frequent Items Mining on Data Streams using Matrix and Scan Reduced Indexing Algorithms

A data stream is used for handling dynamic databases, in which data can arrive continuously without limit. Association rule mining is a data mining technique, used to find the association between the data items in the databases. To generate association rules, frequent items are to be identified from the transactional database. Normally, in data mining, frequent-item-generation algorithms scan the database multiple times. But this is impossible in data streams because it handles dynamic databases. Hence, there is a need to develop a new algorithm, which reduces the number of database scans. In this work, two new algorithms named Scan-Reduced Indexing and Matrix algorithm are proposed for generating frequent itemsets in data streams. Performances of both algorithms are compared based on the execution time and the number of frequent items generated. Experimental results show that the performance of the Scan-Reduced Indexing algorithm is more efficient than that of the Matrix algorithm.

Efficient Mining Support-Confidence Based Framework Generalized Association Rules

Mining association rules are one of the most critical data mining problems, intensively studied since their inception. Several approaches have been proposed in the literature to extend the basic association rule framework to extract more general rules, including the negation operator. Thereby, this extension is expected to bring valuable knowledge about an examined dataset to the user. However, the efficient extraction of such rules is challenging, especially for sparse datasets. This paper focuses on the extraction of literalsets, i.e., a set of present and absent items. By consequence, generalized association rules can be straightforwardly derived from these literalsets. To this end, we introduce and prove the soundness of a theorem that paves the way to speed up the costly computation of the support of a literalist. Furthermore, we introduce FasterIE, an efficient algorithm that puts the proved theorem at work to efficiently extract the whole set of frequent literalets. Thus, the FasterIE algorithm is shown to devise very efficient strategies, which minimize as far as possible the number of node visits in the explored search space. Finally, we have carried out experiments on benchmark datasets to back the effectiveness claim of the proposed algorithm versus its competitors.

Frequent Itemset Generation Using Association Rule Mining Based on Hybrid Neural Network Based Billiard Inspired Optimization

The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald’s as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, [Formula: see text]-measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique.

SCARF: a biomedical association rule finding webserver.

The analysis of enormous datasets with missing data entries is a standard task in biological and medical data processing. Large-scale, multi-institution clinical studies are the typical examples of such datasets. These sets make possible the search for multi-parametric relations since from the plenty of the data one is likely to find a satisfying number of subjects with the required parameter ensembles. Specifically, finding combinatorial biomarkers for some given condition also needs a very large dataset to analyze. For fast and automatic multi-parametric relation discovery association-rule finding tools are used for more than two decades in the data-mining community. Here we present the SCARF webserver for generalized association rule mining. Association rules are of the form: a AND b AND … AND x → y, meaning that the presence of properties a AND b AND … AND x implies property y; our algorithm finds generalized association rules, since it also finds logical disjunctions (i.e., ORs) at the left-hand side, allowing the discovery of more complex rules in a more compressed form in the database. This feature also helps reducing the typically very large result-tables of such studies, since allowing ORs in the left-hand side of a single rule could include dozens of classical rules. The capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer’s database of the Coalition Against Major Diseases (CAMD) in our recent publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300–307, 2017). Here we describe the webserver implementation of the algorithm.

Automatic control method of driving direction of unmanned ground vehicle based on association rules

In order to overcome the problems of large position deviation and angular deviation in the traditional driving direction control method of unmanned ground vehicles, an automatic driving direction control method of unmanned ground vehicles based on association rules is proposed. First, the Apriori algorithm is used to find frequent itemsets of unmanned ground vehicle driving data, generate association rules, and determine the association between itemsets in unmanned ground vehicles (UGV) driving data. After determining the data attributes, the decision tree algorithm is used to complete the mining of UGV driving data. According to the mining results, the proportion integral differential (PID) feedback control algorithm is used to obtain the steering wheel angle control input required for trajectory tracking, and the automatic control of the driving direction is completed. The experimental results show that in various complex traffic environments, the method can control the position deviation of the unmanned ground vehicle between -0.28 m and 0.43 m, and the angle deviation between -0.21 and 0.23 degrees to minimise the deviation. The method in this paper has certain application value and is worthy of further promotion and application.

Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept. This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy. This framework is provided in an interpretable way for document clustering. It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content. Also, this model represents the significance of data features with class discrimination. It is also helpful in measuring the significance of the features during the data clustering process. The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively. Here, DailyMail and DUC 2004 dataset is used to extract the empirical results. The proposed gFAR model gives better trade-off while compared with various prevailing approaches.

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

AbstractNowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre‐processing phase, data pre‐processing, frequent itemset mining, and ARM. In data pre‐processing phase, the collected twitter datasets are pre‐processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid‐19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

IDENTIFYING CUSTOMER BUYING PATTERNS USING MARKET BASKET ANALYSIS

Market Basket Analysis (MBA) is an approach that finds the strength of association between pairs of products that customers buy and can determine patterns of co-occurrence. The main aim of MBA is to determine customer buying behavior and predict next purchase. It can help companies to increase cross-selling.To generate association rules, the Apriori algorithm employs frequently purchased item-sets. It is based on the idea that a frequently purchased item’s subset is also a frequently purchased item. If the support value of a frequently purchased item-set exceeds a minimum threshold, the item-set is chosen. This paper observes the advantages of implementing MBA, algorithms that applies in this technique and ways to identify customer buying patterns.

IPOC: an efficient approach for dynamic association rule generation using incremental data with updating supports

According to recent statistics, there was drastic growth in online business sector where more number of customers intends to purchase items. Due to these retailers accumulates huge volumes of data from day to day operations and engrossed in analyzing the data to watch the behavior of customers at items which strengthen the business promotions and catalog management. It reveals the customer interestingness and frequent items from large data. To carry out this there was known algorithms present which deals with static and dynamic data. Some of them are lag time and memory consuming and involves unnecessary process. This paper intents to implement an efficient incremental pre ordered coded tree (IPOC) generation for data updates and applies frequent item set generation algorithm on the tree. While incremental generation of tree, new data items will link to previous nodes in tree by increasing its support count. This removes the lagging issues in existing algorithms and does not need to mine from scratch and also reduces the time, memory consumption by the use of nodeset data structure. The results of proposed method was observed and analyzed with existing methods. The anticipated method shows improved results by means of generated items, time and memory.

OFGIM: A New Algorithm to Mine Generalized-Itemsets

Most databases mined by a single layer/ crisp association rules algorithms are not flat but contain data in hierarchal/ generalized format. In spite of this fact, a few algorithms available to mine generalized itemsets to produce generalized association rules, GAR, which escort to mine more specific and concrete knowledge for decision makers. This research presents a new algorithm, (Optimized Frequent Generalized Itemsets Miner (OFGIM)), to mine generalized itemsets. Simply, it depends on extending the transactions of a database. The extension is done by adding the parent of an item to the transaction containing the item. The mining process is accomplished by the union of itemsets and the intersection of the tidsets. The algorithm requires two database scans only; the first one is for extending operation and the second scan is for mining process. The proposed algorithm does not need a specified data structure such as hash tree and prunes the apriori-based pruning steps.OFGJM was tested by using six synthetic databases. OFGJM overcomes apriori based algorithm in a ratio of ¼ in all the experiments, but it exhibitsits RAMappealing.

Review of Association Mining Methods for the Extraction of Rules Based on the Frequency and Utility Factors

Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Applications like e-commerce, marketing, healthcare, and web recommendations, etc. consist of items with their utility or profit. Such cases, utility-based itemsets mining methods, are playing a vital role in the generation of effective association rules and are also useful in the mining of high utility itemsets. This paper presents the survey on high-utility itemsets mining methods and discusses the observation study of existing methods with their experimental study using benchmarked datasets.

Improving Association Rule Mining Using Clustering-Based Data Mining Model for Traffic Accidents

Data mining is a process of knowledge discovery to extract the interesting, previously unknown, potentially useful, and nontrivial patterns from large data sets. Currently, there is an increasing interest in data mining in traffic accidents, which makes it a growing new research community. A large number of traffic accidents in recent years have generated large amounts of traffic accident data. The mining algorithms had a great role in determining the causes of these accidents, especially the association rule algorithms. One challenging problem in data mining is effective association rules mining with the huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use the RapidMiner application to design a process that can generate association rules based on clustering algorithms.

Research on CM_HI_pApriori Algorithm Based on Hyperthyroidism

Aiming at the problems of the existing mining algorithm processing data scale, the time complexity of frequent item set storage, and the limitation of the generated association rules, the CM_HI_pApriori (parallel Apriori algorithm of the compressed matrix based on HashMap and Interest) algorithm is proposed. First of all, the parallel data partition method is used to solve the problem of low serialization efficiency. Secondly, HashMap is used to store and query frequent item sets, which makes the time complexity of storing frequent item sets change from linear growth to constant growth. Finally, the interest model is used to solve the problem that association rules do not conform to the actual situation. Experiments show that the CM_HI_pApriori algorithm not only has high scalability and stability in processing large-scale data but also has high accuracy in generating association rules.

Mining a Set of Rules for Determining the Waiting Time for Selling Residential Units

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.