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

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.

Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other information repository. As there are many applications like market basket analysis, detection of fraud in web, medical diagnosis, census data, Customer Relationship Management of credit card business which uses association rules so it is possible to improve the process of Decision making. Security is required for individual transaction and for frequent itemsets when the database is partitioned horizontally among multiple sites. In this case, every site is interested in globally supported association rules without revealing its own local information. To fulfill this goal, We use a secure multi-party algorithm based on secure sum technique to simplify the operation of mining association rule when the database is horizontally partitioned among multiple sites. We are using a Frequent-Pattern (FP) growth algorithm to find frequent itemsets and try to reduce total computation time.

Background: Dry eye disease (DED) has a higher incidence in old age and is seen predominantly in females worldwide. Neurosensory abnormalities, ocular surface inflammation and damage, film instability, and hyperosmolarity are major and proven pathologies responsible for a poor quality of life. Tear breakup time and Schirmer’s I test are predominantly used for the evaluation of primary outcomes in patients undergoing conventional treatment. A previous meta-analysis of some relevant studies proved that combination of acupoints could be more effective than single acupoint treatment. Objectives: The present study aimed to undertake association rule mining and examined the potential kernel acupoint combination in DED treatment constructed from the extracted randomized controlled trials (RCTs) based on a previous meta-analysis. Methods: We summarized 32 acupoints as binary data from the 12 eligible RCTs and analyzed them based on the Apriori algorithm. Results: TE23, BL2, ST2, ST1, EX-HN5, BL1, LI4, ST36, SP6, and KI3 were the 10 most frequently selected acupoints. The major associated rules in combination of acupoints were {TE23, LI4} ≥ {ST1} and {TE23, ST1} ≥ {LI4}, as inferred from 23 association rules. Conclusions: For acupuncture treatment of DED, combined TE23, LI4, and ST1 acupoints could be settled as the kernel of acupoint combination.

The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.

Numerous recommender systems offer recommendations to users depending on their interests. Several applications including those in e-commerce, healthcare and markets have adopted recommendation systems. This paper's major goal is to demonstrate various difficulties with the methods utilized for producing suggestions. The aim is to explore and develop recommendation algorithms using past sales data. It covers the ideas behind content-based filtering, hybrid model recommendation, collaborative filtering and association rules for recommendation systems. For Product recommendation, three algorithms are used: the popularity product recommendation algorithm, the frequent pattern growth algorithm and Apriori algorithm. To increase sales and market response, the proposed recommendation model yields good recommendation outcomes. Additionally, it suggests particular products to potential clients. This paper describes the research environment for recommendation systems in grocery stores. From the observed recommendation models, Popular Product Recommendation does not require customer analysis. Among the models that compare the customer behavior, FP Growth serves better than Apriori model due to its faster execution time. This paper provided a very relevant and practical business transformation scenario that helps businesses in comparable circumstances change their business models.

Uremic pruritus (UP) is a prevalent and troublesome condition affecting individuals with end-stage renal failure, which results in intense pruritus, depression, as well as poor quality of sleep, significantly impacting their quality of life. According to previous studies, acupuncture and acupoint stimulation have been shown to provide additional benefits in treating UP in dialysis patients. In addition, using acupoints combination may yield superior effectiveness compared to utilizing a singular acupoint. To investigate the potential correlations between acupoint combinations, an association-rule analysis was employed. Apriori algorithms stand out as highly potent techniques for identifying associations in databases; this study utilized an association rule mining to examine the association rules of key acupoint groupings that could be employed for treating UP. The analysis utilized information derived from the meta-analysis encompassing 40 randomized controlled trials that used acupuncture to treat UP. In total, 64 acupoints were analyzed, and 71 association rules were found. The following acupoint combinations: Auricular shenmen (TF4), Quchi (LI11), and Geshu (BL17); Auricular heart (Extra14), Sanyinjiao (SP6), and Auricular lung (CO14); and Auricular heart (Extra14), Xuehai (SP10), and Auricular lung (CO14) showed the strongest associations. Acupoints involving Auricular shenmen (TF4), Quchi (LI11), Geshu (BL17), Auricular heart (Extra14), Sanyinjiao (SP6), Auricular lung (CO14), and Xuehai (SP10) can be regarded as the core combination of acupuncture points for managing UP.

BackgroundAlzheimer’s disease (AD) is an ongoing neurological degeneration characterized by amnesia and a decline in cognitive abilities. Hippocampal neurogenesis is the leading cause of AD. Mild cognitive impairment (MCI), a prodromal state of AD, is mainly due to the degradation of neuropsychiatric manifestations. Previous systematic reviews demonstrated that treatment with acupuncture with Chinese herbs is tolerable and effective in improving cognitive function in patients with AD. Our investigation aimed to discover the main acupoint combination for AD management based on a preceding systematic review and meta-analysis of randomized control trials (RCTs).Materials and MethodsOur investigation was executed using association rule analysis, which is a common data mining technique accessible within R. Our study elucidated acupoint locations as binary data from 15 of the included studies using the Apriori algorithm.ResultsThirty-two acupoints were selected from 15 RCTs. The 10 most frequent acupoints were selected. We inspected 503 association rules using the interpreted acupuncture data. The obtained results showed that {SP6, BI10} ≥ {HT7} and {HT7, BI10} ≥ {SP6} were the most associated rules in 15 RCTs.ConclusionThe combination of acupoints ({SP6, BI10} ≥ {HT7} and {HT7, BI10} ≥ {SP6}) can be acknowledged as a core combination for future acupuncture regimens of AD.

This research aims to explore tourist behaviour patterns and develop data-driven tourism route recommendations based on Association Rule Mining (ARM), particularly using the Frequent Pattern Growth (FP-Growth) algorithm. The study focuses on identifying co-occurrence patterns among tourist attractions based on real behavioral data, with the goal of supporting tourism route design and enhancing recommendations for self-guided tourists. The study employed a structured data mining methodology. Raw data was collected from real-world tourist check-ins and GPS traces in Songkhla City, Thailand; encompassing 45 attraction points. The study used the Cross-Industry Standard Process for Data Mining (CRISP-DM) for research methodology. The FP-Growth algorithm was used to discover association rules, using parameters set at support > 0.10, confidence > 0.5, lift > 1.0, and conviction > 1.0 to ensure the relevance and reliability of the extracted rules. The evaluation phase included interpretation of rule quality metrics such as support, confidence, lift, and conviction indicators. Visualizations presented the route recommendations on geographic maps. The results revealed 13 valid association rules involving only 5 out of the 45 locations, suggesting strong behavioral patterns and repeat co-visitation. Interestingly, some of the top-visited locations based on frequency alone did not appear in any of the 13 rules, indicating that frequency-based route planning may not reflect actual tourist behavior. Further analysis showed that all route combinations extracted from the rules had a total travel distance of under 88 kilometers. With Google Map data, this study demonstrates that ARM with FP-Growth can provide insightful patterns for route planning and destination clustering. The proposed method supports smart tourism development by bridging behavioral analytics and operational tourism planning. Stakeholders can apply this approach to periodically update route recommendations to ensure alignment with current tourist behaviors and preferences.

Parallel extraction of association rules from genomics data

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.

Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The application designed can be used to analyze consumer purchase pattern by generating associative rules using FEM algorithm through the Analysis form and printing the consumer purchase pattern through the purchase pattern report.

Objective To evaluate the efficacy of itopride hydrochloride combined with alpha-lipoic acid in treatment of diabetic gastroparesis. Methods One hundred and ten patients with diabetic gastroparesis were randomly assigned to two groups. Sixty patients received α-lipoic acid plus itopride (α-lipoic acid 0.6 g intravenously for 14 d plus itopride 50 mg orally t. i.d), and 50 cases received oral itopride only.The clinical symptoms including postprandial abdominal fullness, belching, acid reflux, anorexia, nausea and vomiting were observed; the gastric emptying rate was tested, and plasma motilin and serum ganstrin levels were measured groups, before and after two weeks of treatment. Results The clinical symptom scores of combination therapy group (4.62±3.46 vs. 12.26±3.43) and control groups (5.43±3.65 vs. 12.31±3.37) were improved after treatment. The overall effective rate of combination group was 91.6% and that of itopride therapy group was 78.0% (χ2=4.098, P<0.05). Gastric emptying rate of combination group [(76.9±9.7)% vs. (37.3±10.2) %] was increased more markedly than that of itopride group[(57.2±10.4)% vs. (36.8±11.2)%] (t=10.221, P<0.05). Plasma motilin and serum ganstrin levels in combination group [(640.2±64.4)ng/L vs. (554.0±67.6)ng/L, (152.6±20.3)ng/L vs. (96.8±22.2)ng/L] were reduced more markedly than those in itopride group [(636.3±66.4)ng/L vs. (589.4±48.3)ng/L, (153.5±19.4)ng/L vs. (120.9±19.7)ng/L] respectively (t=3.100, 5.959, all P<0.05). Conclusion For patients with diabetic gastroparesis, α-lipoic acid and itopride combination therapy can significantly improve symptoms and gastric emptying rate, decrease motlin and gastrin levels; and the efficacy of combination therapy is better than that of itopride monotherapy. Key words: Diabetes mellitus; Gastroparesis; Tiapride; Thioctic acid

Background:Diabetic gastroparesis (DG) is a common autonomic neuropathy which impacts on nutritional state and quality of life in diabetic patients, and it also adversely affects glycemic control in diabetes. The prevalence of DG is growing with the number of patients with diabetes continues to increase. However, there is no definitive answer as to which of the current therapies is the best for the clinical treatment of the different manifestations of DG. The subject of this study is to answer the following question: what is the best intervention for diabetic patients with gastroparesis?Methods:Comprehensive searches of the Cochrane Library, PubMed, Embase, Medline, Central and Web of Science, and 4 Chinese databases, including China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Chinese Biomedical Literature Database, and WanFang will be completed using the following keywords DG and therapies and related entry terms. Studies will be included, according to the eligibility criteria (randomized controlled trials and controlled clinical trials, considering specific outcome measures for DG). The reference lists of included studies will be manual searched. Relevant data will be extracted from included studies using a specially designed data extraction sheet. Risk of bias of the included studies will be assessed, and the overall strength of the evidence will be summarized through GRADE. A random effects model will be used for all pairwise meta-analyses (with a 95% confidence interval). A Bayesian network meta-analysis will explore the relative benefits between the various therapies. The review will be reported using the Preferred Reporting Items for Systematic Reviews incorporating Network Meta-Analyses statement. Network meta-analysis will be performed using a Bayesian framework through the Winbugs software.Results:This network meta-analysis will identify the best effective therapy for DG.Conclusion:This study will compare and evaluate current therapies for DG, and find the best treatment of DG.

In a business, it takes effort to maximize profits. Among them by doing promotions. Minimarket Ulfamart has not done innovation in the form of promotion of frugal packages to customers. The precision of promotion can be learned from the database of a retail company primarily shopping patterns on products commonly purchased simultaneously. Information about inaccurate customer spending patterns results in inaccurate and efficient promotional policies. One of the most common attempts to acquire and explore customer spending patterns is to use data mining known as Knowledge Discovery in Database (KDD). One of the data mining techniques is the Association Rule which is a procedure in Market Basket Analysis. Market basketball is defined as an itemset purchased simultaneously by the customer in a transaction. Market basket analysis is a powerful tool for cross-selling strategies. A pattern is determined by two parameters, namely support (value of support) and confidence (value of certainty). The Frequent Pattern Growth (FP-Growth) algorithm is used to help find some association rules from the database by applying a Tree Tree structure or called FP-Tree. Implementation using RapidMiner to help find accurate patterns to get a combination of items that can be used as a frugal package.

Mining frequent patterns in transaction databases has been a popular subject in data mining research. Common activities include finding patterns in database transactions, times-series, and exceptions. The Apriori algorithm is a widely accepted method of generating frequent patterns. The algorithm can require many scans of the database and can seriously tax resources. New methods of finding association rules, such as the Frequent Pattern Tree (FP-Tree) have improved performance, but still have problems when new data becomes available and require two scans of the database.This paper proposes a new method, which requires only one scan of the database and supports update of patterns when new data becomes available. We design a new structure called Pattern Repository (PR), which stores all of the relevant information in a highly compact form and allows direct derivation of the FP-Tree and association rules quickly with a minimum of resources. In addition, it supports run-time generation of association rules by considering only those patterns that meet on-line data requirements.