scholarly journals Generation of Association Rules of Data Mining for Lung Cancer by Air Pollution

2020 ◽  
Vol 9 (3) ◽  
pp. 2874-2879
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Air Pollutants ◽  
Tamil Nadu ◽  
Respiratory Illness ◽  
Atmospheric Pollutants ◽  
Healthcare Database ◽  
Rapid Urbanization ◽  
Association Rules Mining ◽  
Data Mining Approach

Revelation to adverse air pollutants attributed harmful effects in humans health. This research targets to evaluate the influence of atmospheric pollutants via determining the number of hospitalization underlying pulmonary complication in Chennai, Tamil Nadu. This tropical metropolitan city and also capital of Tamil Nadu have recently endured with the atmospheric pollutants. Due to rapid urbanization, followed by installation of numerous industries over the years have gradually affected the air quality. Chennai has respiratory illness in maximum record owing to atmospheric pollutants. The atmospheric pollutants and its impact on wellbeing could be due to pollutant’s ability in inducing oxidative stress, allergy and irritation, and it is reasonable that high points for air pollutants is producing hospitalization in great number. In this paper, a efficacious and novel study utilizing data mining approach involving ‘suggestion rules’ had imparted, wherein its capability to search for an fundamental linking among qualities with greater database and the capacity to handle inexact database that frequently happens under real world scenario which appeared rapidly problematic. A detection of association dealings, regular designs or connections between items set or components in databases is association rules mining. Association rules are very beneficial in atmospheric pollutants and healthcare database because they deal prospect to lead smart analysis and produce valuable data also frame important data bases rapidly and routinely, so that progress effective plans to minimize health contact to the atmospheric pollutants. Data completed pre-processing phase to assist condition of demonstrating procedure. With respect to conclusion, association rules mining had performed by Apriori, Eclat and FP growth algorithm the results showed that the latter was much accurate and consumes lesser time

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

2012 ◽  
Vol 3 (2) ◽  
pp. 24-41 ◽  
Author(s):  
Tutut Herawan ◽  
Prima Vitasari ◽  
Zailani Abdullah
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Teaching Strategies ◽  
Engineering Students ◽  
Mining Method ◽  
Association Rules Mining ◽  
Quality Of Learning ◽  
Pattern Growth

One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth (Significant Least Pattern Growth) for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang (UMP). The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Application of Association Rules Mining in Employment Guidance

2012 ◽  
Vol 479-481 ◽  
pp. 129-132
Author(s):  
Lei Wang ◽  
Cun Xiao Yi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Important Task ◽  
Complex Data ◽  
Employment Rate ◽  
Association Rules Mining ◽  
Mining Association Rules ◽  
Vocational Colleges

How to improve the employment rate of graduates is an important task for higher vocational colleges to solve. In order to effectively improve their Employment competitiveness, advice should be made to help students to enhance specific kinds of learning and ability. Association Rules Mining is one core of the Data Mining Association Rules, it’s helpful in finding useful information hidden in complex data. By using Association Rules Mining in finding the knowledge and ability which helps employed students to earn their jobs, necessary ability for each kind of job can be found, and then advice offered for students to target their employment career will be more exact and proper.

Data to Knowledge-Based Transformation

2021 ◽  
Vol 13 (4) ◽  
pp. 141-152
Author(s):  
Fauzan Asrin ◽  
*Saide Saide ◽  
Silvia Ratna
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Industrial Sector ◽  
Growth Patterns ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Data Mining Approach ◽  
Important Evidence ◽  
Confidence Value ◽  
Computer Maintenance

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

The Discussion on the Improved Association Rules Algorithm in Data Mining

2014 ◽  
Vol 989-994 ◽  
pp. 1985-1988
Author(s):  
Tao Wen ◽  
Li Sun ◽  
Li Zhu
Keyword(s):  
Data Mining ◽  
Pattern Matching ◽  
Association Rules ◽  
Potential Application ◽  
Huge Amount ◽  
Association Rules Mining ◽  
Classical Association ◽  
Improved Algorithm

The data mining can help to extract the information and knowledge with potential application value from a huge amount of data. In order to do the date mining scientifically and efficiently, this article provides an improved algorithm based on the classical association rules Aprior algorithm, taking advantage of which, we can engage in the association rules mining of the database to obtain the useful information. The improved algorithm avoids pattern matching and reduces the number of times of visiting the database, thus improves the speed of date mining to some extend.

An Efficient Graph-Based Method for Parallel Mining Problems

2004 ◽  
Vol 03 (02) ◽  
pp. 143-154
Author(s):  
Chin-Chen Chang ◽  
Chih-Yang Lin ◽  
Pei-Yu Lin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Load Balance ◽  
Experimental Results ◽  
Memory Usage ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Mining Algorithms

Parallel association rules mining is a noticeable problem in data mining. However, little work has been proposed to deal with three important issues: (1) less memory usage; (2) less communication, among the involved computers, over the network; and (3) load balance among computers. In this paper, we present a graph-based scheme to solve the parallel mining problem by applying independent groups (clusters of maximal cliques). To bring the three issues to a close, the purpose of the independent groups aims at dividing a database into several independent sub-databases, so each sub-database can be employed independently to perform mining algorithms. To emphasis the effectiveness of the graph-based scheme, we adopt the independent groups not only for maximal large itemsets mining but also for general large itemsets mining. The experimental results show that our scheme can improve the efficiency for parallel mining when the independent groups are well-organized and designed.

Privacy Preserving in Association Rules Mining with VPA Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 3060-3063 ◽  
Author(s):  
Yi Tao Zhang ◽  
Wen Liang Tang ◽  
Cheng Wang Xie ◽  
Ji Qiang Xiong
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Privacy Preserving Data Mining ◽  
Association Rules Mining ◽  
Synthetic Datasets

A VPA algorithm is proposed to mining the association rules in the privacy preserving data mining, where data is vertically partitioned. The VSS protocol was used to encrypt the vertically data, which was owned by different parties. And the private comparing protocol was adopted to generate the frequent itemset. In VPA the ID numbers of the recordings were employed to keep the consistency of the data among different parties, which were saved in ID index array. The VPA algorithm can generate association rules without violating the privacy. The performance of the scheme is validated against representative real and synthetic datasets. The results reveal that the VPA algorithm can do the same in finding frequent itemset and generating the consistent rules, as it did in Apriori algorithm, in which the data were vertically partitioned and totally encrypted.

Different Types of Association Rules Mining Review

2012 ◽  
Vol 241-244 ◽  
pp. 1589-1592
Author(s):  
Jun Tan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Application Systems ◽  
Different Types ◽  
Multi Level

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

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