scholarly journals Analysis of acupoint selection and combinations in acupuncture treatment of asthma based on data mining

2021 ◽  
Author(s):  
Pan-Pan Shang ◽  
Cai-Tao Chen ◽  
Mi Cheng ◽  
Yang-Lin Shi ◽  
Yong-Qing Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Acupuncture Treatment ◽  
Interconnection Network ◽  
Rule Mining ◽  
Bladder Meridian ◽  
Using Data ◽  
Conception Vessel ◽  
Selection Of

Objective: Using data mining, the present study aimed to discover the most effective acupoints and combinations in the acupuncture treatment of asthma. Methods: The main acupoints prescribed in these clinical trials was collected and quantified. A network analysis was performed to uncover the interconnections. Additionally, hierarchical clustering analysis and association rule mining were conducted to discover the potential acupoint combinations. Results: Feishu (BL13), Dingchuan (EX-B1), Dazhui (GV14), Shengshu (BL23), Pishu (BL20), and Fengmen (BL12) appeared to be the most frequently used acupoints for asthma. While the Bladder Meridian of Foot Taiyang, the Governor Vessel, and the Conception Vessel, compared to other meridians, were found to be the more commonly selected meridians. In the acupoint interconnection network, Feishu (BL13), Fengmen (BL12), Dingchuan (EX-B1), and Dazhui (GV14) were defined as key node acupoints. Association rule mining analysis demonstrated that the combination of Pishu, Shenshu, Feishu, and Dingchuan, as well as that of Feishu, Dazhui, and Fengmen were potential acupoint combinations that should be selected with priority in asthma treatment. Conclusion: This study provides valuable information regarding the selection of the most effective acupoints and combinations for clinical acupuncture practice and experimental study aimed at the prevention and treatment of asthma.

scholarly journals Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Asthma Based on Data Mining

2021 ◽  
Author(s):  
Pan-Pan Shang ◽  
Cai-Tao Chen ◽  
Mi Cheng ◽  
Yang-Lin Shi ◽  
Yong-Qing Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Clinical Trials ◽  
Association Rule ◽  
Clustering Analysis ◽  
Association Rule Mining ◽  
Acupuncture Treatment ◽  
Interconnection Network ◽  
Therapeutic Modality ◽  
Therapeutic Effects ◽  
Rule Mining

Abstract Background: Asthma is a highly prevalent respiratory disease that remains difficult to control. Acupuncture, as an important alternative therapeutic modality in preventing and treating asthma, is widely used in the world due to its promising efficacy and safety. Although acupoint selection and combinations are critical to therapeutic effects of acupuncture, its fundamental rules for asthma have not been fully understood. Thus, using data mining, the present study aimed to discover the most effective acupoints and combinations in the acupuncture treatment of asthma..Methods: Controlled clinical trials (CCTs) of acupuncture treatment for asthma were searched and retrieved from databases including Chinese National Knowledge Infrastructure (CNKI), Wanfang, and PubMed. Data regarding the main acupoints prescribed in these clinical trials was collected and quantified. A network analysis was performed to uncover the interconnections between the acupoints. Additionally, hierarchical clustering analysis and association rule mining were conducted to discover the potential acupoint combinations. Results: A total of 183 CCTs were retrieved. Feishu (BL13), Dingchuan (EX-B1), Dazhui (GV14), Shengshu (BL23), Pishu (BL20), and Fengmen (BL12) appeared to be the most frequently used acupoints for asthma. While the Bladder Meridian of Foot Taiyang, the Governor Vessel, and the Conception Vessel, compared to other meridians, were found to be the more commonly selected meridians. In the acupoint interconnection network, Feishu (BL13), Fengmen (BL12), Dingchuan (EX-B1), and Dazhui (GV14) were defined as key node acupoints. Moreover, acupoint clustering analysis revealed the treatment principle of “facilitating the flow of the lung Qi, tonifying spleen and kidney, and treating both the symptoms and root causes”. Association rule mining analysis demonstrated that the combination of Pishu, Shenshu, Feishu, and Dingchuan, as well as that of Feishu, Dazhui, and Fengmen were potential acupoint combinations that should be selected with priority in asthma treatment.Conclusion: Based on a data mining analysis of published CCTs, this study provides valuable information regarding the selection of the most effective acupoints and combinations for clinical acupuncture practice and experimental study aimed at the prevention and treatment of asthma.

Optimal Inventory Classification using Data Mining Techniques

2016 ◽  
pp. 236-255 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Consumer Behavior Analysis ◽  
Relationship Extraction ◽  
Using Data ◽  
Research Studies

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

scholarly journals Study of high yielding crops cultivation in India using data mining techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 121
Author(s):  
M J Carmel Mary Belinda ◽  
Umamaheswari R ◽  
Alex David S
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Bayesian Belief Network ◽  
Support Vector ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Using Data

Data mining in agriculture is a modern and emerging research technique. Data mining provide many techniques like k means algorithm, support vector machine, association rule mining and Bayesian belief network [1]. This technique can be used in agriculture for various purposes. This paper describes about how association rules mining and apriori algorithm can be used in agriculture field. This paper also describes about soil, its types and crops grown in each type of soil. The technique that has been used here can be a rough set study, but like this many efficient techniques can be applied to solve many problems in agriculture.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

scholarly journals ASSOCIATION RULE MINING UNTUK MENINGKATKAN PROMOSI PRODUK ( STUDI KASUS PADA PD. XYZ )

2018 ◽  
Vol 7 (2) ◽  
pp. 284-288
Author(s):  
Doni Winarso ◽  
Anwar Karnaidi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

Analisis association rule adalah teknik data mining yang digunakan untuk menemukan aturan asosiatif antara suatu kombinasi item. penelitian ini menggunakan algoritma apriori. Dengan  algoritma tersebut dilakukan pencarian  frekuensi dan item barang yang paling sering muncul. hasil dari penelitian in menunjukkan bahwa algoritma apriori  dapat digunakan untuk menganalisis data transaksi sehingga diketahui mana produk yang harus  dipromosikan. Perhitungan metode apriori menghasilkan suatu pola pembelian yang terjadi di PD. XYZ. dengan menganalisis pola tersebut dihasilakn kesimpulan bahwa produk  yang akan dipromosikan yaitu cat tembok ekonomis dan peralatan cat berupa kuas tangan dengan nilai support 11% dan confidence 75% .

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

2021 ◽  
pp. 237-253
Author(s):  
M. Nandhini ◽  
S. N. Sivanandam ◽  
S. Renugadevi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Rule Mining ◽  
Healthcare Data ◽  
Bee Colony ◽  
Small Set ◽  
Significant Class

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

Constraint-Based Association Rule Mining

2011 ◽  
pp. 307-312 ◽  
Author(s):  
Carson Kai-Sang Leung
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Computational Cost ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
The Subject ◽  
User Focus ◽  
High Computational Cost

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.

Association Rule Mining of Relational Data

2011 ◽  
pp. 87-93
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Data Structures ◽  
Association Rule ◽  
Association Rule Mining ◽  
Relational Data ◽  
Rule Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Relational Database Management ◽  
Relational Database Management Systems

Most data of practical relevance are structured in more complex ways than is assumed in traditional data mining algorithms, which are based on a single table. The concept of relations allows for discussing many data structures such as trees and graphs. Relational data have much generality and are of significant importance, as demonstrated by the ubiquity of relational database management systems. It is, therefore, not surprising that popular data mining techniques, such as association rule mining, have been generalized to relational data. An important aspect of the generalization process is the identification of challenges that are new to the generalized setting.

On Association Rule Mining for the QSAR Problem

2011 ◽  
pp. 83-86
Author(s):  
Luminita Dumitriu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Neuro Fuzzy ◽  
The 1960S ◽  
New Compounds

The concept of Quantitative Structure-Activity Relationship (QSAR), introduced by Hansch and co-workers in the 1960s, attempts to discover the relationship between the structure and the activity of chemical compounds (SAR), in order to allow the prediction of the activity of new compounds based on knowledge of their chemical structure alone. These predictions can be achieved by quantifying the SAR. Initially, statistical methods have been applied to solve the QSAR problem. For example, pattern recognition techniques facilitate data dimension reduction and transformation techniques from multiple experiments to the underlying patterns of information. Partial least squares (PLS) is used for performing the same operations on the target properties. The predictive ability of this method can be tested using cross-validation on the test set of compounds. Later, data mining techniques have been considered for this prediction problem. Among data mining techniques, the most popular ones are based on neural networks (Wang, Durst, Eberhart, Boyd, & Ben-Miled, 2004) or on neuro-fuzzy approaches (Neagu, Benfenati, Gini, Mazzatorta, & Roncaglioni, 2002) or on genetic programming (Langdon, &Barrett, 2004). All these approaches predict the activity of a chemical compound, without being able to explain the predicted value. In order to increase the understanding on the prediction process, descriptive data mining techniques have started to be used related to the QSAR problem. These techniques are based on association rule mining. In this chapter, we describe the use of association rule-based approaches related to the QSAR problem.

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