Mining Co-Occurrence Patterns among Deep Road Distresses Using Association Rule Analysis

In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum–Cunge scheme.

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ASSOCIATION RULE ANALYSIS FOR TOUR ROUTE RECOMMENDATION AND APPLICATION TO WCTSNOP

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1121-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1121-1126

Author(s):

H. Fang ◽

C. Chen ◽

J. Lin ◽

X. Liu ◽

D. Fang

Keyword(s):

Association Rule ◽

Cultural Tourism ◽

Strategic Decisions ◽

Service Network ◽

Network Operation ◽

Route Recommendation ◽

Rule Analysis ◽

Study Association ◽

Tourism Service ◽

Operation Platform

The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality.

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An Association Rule Analysis of the Acupressure Effect on Sleep Quality

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/1399258 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Chih-Hung Lin ◽

Ya-Hsuan Lin ◽

I-Shiang Tzeng ◽

Chan-Yen Kuo

Keyword(s):

Sleep Quality ◽

Association Rule ◽

Binary Data ◽

Meta Analysis ◽

Subjective Assessment ◽

Rule Mining ◽

Rule Analysis ◽

Improve Sleep Quality ◽

Important Physiological Process ◽

Perceived Sleep Quality

Background. Sleep is recognized as an all-important physiological process, which also contributes to maintaining several bodily functions and systems. According to the Pittsburgh Sleep Quality Index (PSQI), also known as the most widely used tool in the field of subjective assessment of self-perceived sleep quality, a combination of acupoints could be more effective than single acupoint treatment in improving sleep quality. Methods. The present study was based on the extracted eligible studies rooted in a previous meta-analysis that worked on the basis of association rule mining and examined the potential kernel acupoint combinations for improving sleep quality. Results. Depending on the Apriori algorithm, we summarized 26 acupoints as binary data from the 32 eligible studies based on a previous meta-analysis and analyzed them. The top 10 most frequently selected acupoints were HT7, SP6, PC6, KI1, GV20, EM5, EX-HN3, EX-HN16, KI3, and MA-TF1. Furthermore, as deduced from 21 association rules, the primary relevant rules in the combination of acupoints are (EX-HN3, EX-HN16)=>(GV20) and (HT7, KI1)=>(PC6). Conclusions. In order to use acupuncture to improve sleep quality, integrating (EX-HN3, EX-HN16, GV20) with (HT7, KI1, PC6) acupoints could be deemed as the kernel acupoint combination.

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Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i3.3064 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1107

Author(s):

Siti Nurlela ◽

Lilyani Asri Utami

Keyword(s):

Data Mining ◽

Marketing Strategy ◽

Automotive Industry ◽

Association Rule ◽

The Other ◽

Apriori Algorithm ◽

Sales Data ◽

Competitive Prices ◽

Rule Analysis ◽

Strategy Pattern

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

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