FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

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Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4107.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 309-316

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Healthcare Data ◽

Data Mining Approach ◽

Mining Community ◽

Pattern Growth ◽

Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

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Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

International Journal of Educational Research & Social Sciences ◽

10.51601/ijersc.v2i1.45 ◽

2021 ◽

Vol 2 (1) ◽

pp. 132-139

Author(s):

Wiwit Pura Nurmayanti ◽

Hanipar Mahyulis Sastriana ◽

Abdul Rahim ◽

Muhammad Gazali ◽

Ristu Haiban Hirzi ◽

...

Keyword(s):

Association Rules ◽

Association Rule ◽

Frequent Pattern ◽

Apriori Algorithm ◽

Market Basket Analysis ◽

Market Basket ◽

Transaction Data ◽

Pattern Growth ◽

Product Sales ◽

The Relationship

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

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An Apriori Algorithm-Based Association Rule Analysis to Identify Acupoint Combinations for Treating Diabetic Gastroparesis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6649331 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Ping-Hsun Lu ◽

Jui-Lin Keng ◽

Fu-Ming Tsai ◽

Po-Hsuan Lu ◽

Chan-Yen Kuo

Keyword(s):

Association Rules ◽

Association Rule ◽

Frequent Pattern ◽

Diabetic Gastroparesis ◽

Apriori Algorithm ◽

Frequency Pattern ◽

Potential Association ◽

Pattern Growth ◽

Core Elements ◽

Rule Analysis

We explored the potential association rules within acupoints in treating diabetic gastroparesis (DGP) using Apriori algorithm complemented with another partition-based algorithm, a frequent pattern growth algorithm. Apriori algorithm is a data mining-based analysis that is widely applied in various fields, such as business and medicine, to mine frequent patterns in datasets. To search for effective acupoint combinations in the treatment of DGP, we implemented Apriori algorithm to investigate the association rules of acupoints among 17 randomized controlled trials (RCTs). The acupoints were extracted from the 17 included RCTs. In total, 29 distinct acupoints were observed in the RCTs. The top 10 frequently selected acupoints were CV12, ST36, PC6, ST25, BL21, BL20, BL23, SP6, BL18, and ST21. The frequency pattern of acupoints achieved by using a frequent pattern growth algorithm also confirms the result. The results showed that the most associated rules were {BL23, BL18} ≥ {SP6}, {BL20, BL18} ≥ {PC6}, {PC6, BL18} ≥ {BL20}, and {SP6, BL18} ≥ {BL23} in the database. Acupoints, including BL23, BL18, SP6, BL20, and PC6, can be deemed as core elements of acupoint combinations for treating DGP.

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Marketing Strategy Using Frequent Pattern Growth

Journal Of Computer Networks, Architecture and High Performance Computing ◽

10.47709/cnahpc.v3i2.1039 ◽

2021 ◽

Vol 3 (2) ◽

pp. 194-201

Author(s):

Nazori Suhandi ◽

Rendra Gustriansyah

Keyword(s):

Association Rules ◽

Marketing Strategies ◽

Frequent Pattern ◽

High Positive Correlation ◽

Pattern Growth ◽

Growth Method ◽

Mining Methods ◽

The Right ◽

The Impact

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.

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A Survey on Fuzzy Association Rule Mining

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2013010101 ◽

2013 ◽

Vol 9 (1) ◽

pp. 1-27 ◽

Cited By ~ 13

Author(s):

Harihar Kalia ◽

Satchidananda Dehuri ◽

Ashish Ghosh

Keyword(s):

Association Rules ◽

Association Rule ◽

Quantitative Data ◽

Association Rule Mining ◽

Real Life ◽

Rule Mining ◽

Fuzzy Association Rules ◽

Fuzzy Association Rule ◽

Mining Algorithms ◽

Fuzzy Association Rule Mining

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

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

International Journal of Information Technology Project Management ◽

10.4018/ijitpm.2021100101 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1-10

Author(s):

Subba Reddy Meruva ◽

Venkateswarlu Bondu

Keyword(s):

Association Rules ◽

Association Rule ◽

Strong Association ◽

Vital Role ◽

Frequent Pattern ◽

Association Mining ◽

Observation Study ◽

Mining Methods ◽

High Utility ◽

High Utility Itemsets

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.

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Frequent Pattern Discovery and Association Rule Mining of XML Data

Data Mining ◽

10.4018/978-1-4666-2455-9.ch044 ◽

2013 ◽

pp. 859-879

Author(s):

Qin Ding ◽

Gnanasekaran Sundarraj

Keyword(s):

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Pattern Discovery ◽

Frequent Pattern ◽

Future Research ◽

Frequent Patterns ◽

Rule Mining ◽

Xml Data ◽

Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

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Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539317400058 ◽

2017 ◽

Vol 24 (06) ◽

pp. 1740005 ◽

Cited By ~ 3

Author(s):

Meera Sharma ◽

Abhishek Tandon ◽

Madhu Kumari ◽

V. B. Singh

Keyword(s):

Operating System ◽

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Clustering Algorithm ◽

Large Data ◽

Software Project ◽

Rule Mining ◽

Data Set ◽

Bug Reports

Bug triaging is a process to decide what to do with newly coming bug reports. In this paper, we have mined association rules for the prediction of bug assignee of a newly reported bug using different bug attributes, namely, severity, priority, component and operating system. To deal with the problem of large data sets, we have taken subsets of data set by dividing the large data set using [Formula: see text]-means clustering algorithm. We have used an Apriori algorithm in MATLAB to generate association rules. We have extracted the association rules for top 5 assignees in each cluster. The proposed method has been empirically validated on 14,696 bug reports of Mozilla open source software project, namely, Seamonkey, Firefox and Bugzilla. In our approach, we observe that taking on these attributes (severity, priority, component and operating system) as antecedents, essential rules are more than redundant rules, whereas in [M. Sharma and V. B. Singh, Clustering-based association rule mining for bug assignee prediction, Int. J. Business Intell. Data Mining 11(2) (2017) 130–150.] essential rules are less than redundant rules in every cluster. The proposed method provides an improvement over the existing techniques for bug assignment problem.

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