scholarly journals Data Mining Technique: An Implementation of Association Rule Mining in Healthcare

IARJSET ◽  
2017 ◽  
Vol 4 (7) ◽  
pp. 62-65 ◽  
Author(s):  
Ashwini Rajendra Kulkarni ◽  
Dr. Shivaji D. Mundhe
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

scholarly journals Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

scholarly journals Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System

2019 ◽  
Vol 8 (6) ◽  
pp. 2625-2628
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Models ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.

ANALYSIS OF MESENCHYMAL STEM CELL DIFFERENTIATION IN VITRO USING CLASSIFICATION ASSOCIATION RULE MINING

2009 ◽  
Vol 07 (06) ◽  
pp. 905-930 ◽  
Author(s):  
WEIQI WANG ◽  
YANBO JUSTIN WANG ◽  
RENÉ BAÑARES-ALCÁNTARA ◽  
FRANS COENEN ◽  
ZHANFENG CUI
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Stem Cell Differentiation ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Web Based ◽  
Mesenchymal Stem Cell Differentiation ◽  
Classification Prediction

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

scholarly journals An improved association rule mining algorithm for large data

2021 ◽  
Vol 30 (1) ◽  
pp. 750-762
Author(s):  
Zhenyi Zhao ◽  
Zhou Jian ◽  
Gurjot Singh Gaba ◽  
Roobaea Alroobaea ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rapid Development ◽  
Large Data ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technology ◽  
Database Technology

Abstract The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm.

An Enhanced Fuzzy ARM Approach for Intrusion Detection

2011 ◽  
Vol 3 (2) ◽  
pp. 41-61 ◽  
Author(s):  
Nasser S. Abouzakhar ◽  
Huankai Chen ◽  
Bruce Christianson
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Intrusion Detection ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Detection Model ◽  
Improved Algorithm

The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic dataset. Moreover, the authors present an improved algorithm named Matrix Fuzzy ARM algorithm for mining fuzzy association rules. The experiments and results that are presented in this paper demonstrate the effectiveness of integrating fuzzy logic with association rule mining in intrusion detection. The performance of the developed detection model is improved by using this integrated approach and improved algorithm.

scholarly journals Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu

2019 ◽  
Vol 8 (11S) ◽  
pp. 164-168
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Tamil Nadu ◽  
Vital Role ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data ◽  
Work Done

Data mining plays an essential role in the cropproduction. It is a major field for forecasting and analyzing the crop. The vital role of the cultivator is to know about the production of the crop. In the years before, forecasting was carried out by taking into account the cultivator’s previous experience on the selected area. The forecasting was the important criteria which should be solved by considering the data on hand. By using Data mining method, the enhanced selection can be done. Various Data Mining methods have been used for calculating the upcoming year's production. This investigation helps to recommend a model for forecasting the yield from the earlier data. For accomplishing and forecasting the yield association rule mining in data mining has been used. This helps to focus on implementing a system which may be used for forecasting the yield in the upcoming years. This research aims at presenting a detailed study by forecasting the yield using association rules in data mining technique for the chosen area in India. The results haveshown that the anticipated work done is working well in order to predict the production of the yield.

scholarly journals An application of data mining techniques in designing catalogue for a laundry service

2018 ◽  
Vol 154 ◽  
pp. 01099
Author(s):  
Annisa Uswatun Khasanah ◽  
Deliana Ardhitama Erlangga ◽  
Ahmad Mustopa Jamil
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Strong Relationship ◽  
Target Market ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Interesting Pattern ◽  
Customer Segment ◽  
The Media

Catalogues are the media that companies use to promote their products or services. Since catalogue is one of marketing media, the first essential step before designing product catalogue is determining the market target. Besides, it is also important to put some information that appeal to the target market, such as discount or promos by analysing customer pattern preferences in using services or buying product. This study conduct two data mining technique. The first is clustering analysis to segment customer and the second one is association rule mining to discover an interesting pattern about the services that commonly used by the customer at the same service time. Thus, the results will be used as a recommendation to make an attractive marketing strategy to be put in the service catalogue promo for a laundry in Sleman Yogyakarta. The clustering result showed that the biggest customer segment is university student who come 3 until 5 times in a month on weekends, while the association rule result showed that clothes, shoes, and bed sheet have strong relationship. The catalogue design is presented in the end of the paper.

scholarly journals Security and privacy concerned association rule mining technique for the accurate frequent pattern identification

2017 ◽  
Vol 7 (1.1) ◽  
pp. 19
Author(s):  
T. Nusrat Jabeen ◽  
M. Chidambaram ◽  
G. Suseendran
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Simulation Environment ◽  
Rule Mining ◽  
Mining Technique ◽  
Diffie Hellman

Security and privacy has emerged to be a serious concern in which the business professional don’t desire to share their classified transaction data. In the earlier work, secured sharing of transaction databases are carried out. The performance of those methods is enhanced further by bringing in Security and Privacy aware Large Database Association Rule Mining (SPLD-ARM) framework. Now the Improved Secured Association Rule Mining (ISARM) is introduced for the horizontal and vertical segmentation of huge database. Then k-Anonymization methods referred to as suppression and generalization based Anonymization method is employed for privacy guarantee. At last, Diffie-Hellman encryption algorithm is presented in order to safeguard the sensitive information and for the storage service provider to work on encrypted information. The Diffie-Hellman algorithm is utilized for increasing the quality of the system on the overall by the generation of the secured keys and thus the actual data is protected more efficiently. Realization of the newly introduced technique is conducted in the java simulation environment that reveals that the newly introduced technique accomplishes privacy in addition to security.

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

Sign in / Sign up

Export Citation Format

Share Document