scholarly journals Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications

2021 ◽  
Vol 23 (06) ◽  
pp. 12191-235
Author(s):  
John Vaseekaran ◽  
◽  
Dr. N. Srinivasan ◽  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Smartphone Apps ◽  
Smartphone Applications ◽  
Industry Data ◽  
Rule Mining ◽  
Vast Number ◽  
Customer Interface

The demand for smartphone apps has grown with the rising interest in artificial intelligence. Thanks to a vast number of applicant service applications, choosing the smartphone apps you want to use has been very complex for consumers. It is therefore essential that the customer interface is improved and that individual suggestions are made. Conventional recommendation approaches can in some cases be effective but have some drawbacks, which generally lead to unreliable recommendations. This study provides a basis for recommending smartphone applications, which is built on the algorithm of Hyperlink Induced Topic Search (HITS) in conjunction with association rule mining in this context. The approach combines the scores of authority and hub into the applications by means of downloads and ratings and not only takes into account the role of smartphone apps in alliance rules but also the trustworthiness aspect of consumers. Studies with industry data sets from the Samsung framework reveal that the proposed approach increases the recommendation precision greatly relative to conventional approaches.

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

APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION

2008 ◽  
Vol 18 (08) ◽  
pp. 1043-1061 ◽  
Author(s):  
LIGUO YU ◽  
STEPHEN R. SCHACH
Keyword(s):  
Association Rule ◽  
Software Maintenance ◽  
Association Rule Mining ◽  
Concept Drift ◽  
Training Data ◽  
Software Systems ◽  
Association Mining ◽  
Change Propagation ◽  
Data Sets ◽  
Rule Mining

A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

The Novel Model of Construct Materials Science and Information Based on Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Materials Science ◽  
Frequent Itemsets ◽  
Data Sets ◽  
The Novel ◽  
Rule Mining ◽  
Novel Model

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

2015 ◽  
pp. 191-195
Author(s):  
K.GANESH KUMAR ◽  
H.VIGNESH RAMAMOORTHY ◽  
M.PREM KUMAR ◽  
S. SUDHA
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
Research Area ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

scholarly journals Association Rule Mining on Big Data Sets

2020 ◽  
Author(s):  
Oguz Celik ◽  
Muruvvet Hasanbasoglu ◽  
Mehmet S. Aktas ◽  
Oya Kalipsiz
Keyword(s):  
Big Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Rule Mining

Association Rule Mining in Developmental Psychology

Data Mining ◽  
2013 ◽  
pp. 1737-1751
Author(s):  
D. A. Nembhard ◽  
K. K. Yip ◽  
C. A. Stifter
Keyword(s):  
Time Series ◽  
Association Rule ◽  
Association Rule Mining ◽  
Developmental Psychology ◽  
Temporal Characteristic ◽  
Data Sets ◽  
Rule Mining ◽  
Dynamic Interactions ◽  
Series Type ◽  
Type Data

Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current methodologies used in this field to study developmental processes include Yule’s Q, state space grids, time series analysis, and lag analysis. The data collected in this field are often time-series-type data. Applying association rule mining in developmental psychology is a new concept that may have a number of potential benefits. In this paper, two sets of infant-mother interaction data sets are examined using association rule mining. Previous analyses of these data used conventional statistical techniques. However, they failed to capture the dynamic interactions between the infant-mother pair as well as other issues relating to the temporal characteristic of the data. Three approaches are proposed in this paper as candidate means of addressing some of the questions that remain from previous studies. The approaches used can be applied to association rule mining to extend its application to data sets in related fields.

Study on Building Wall Materials System by Using Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 193-196
Author(s):  
Hong Sheng Xu ◽  
Qing Tan ◽  
Chao Li
Keyword(s):  
Association Rule ◽  
Industrial Waste ◽  
Association Rule Mining ◽  
Building Blocks ◽  
Data Sets ◽  
Clay Brick ◽  
Rule Mining ◽  
Construction Techniques ◽  
Building Wall ◽  
Wall Materials

Association rule mining is to find interesting associations between itemsets in large amounts of data or related links. The new wall materials are mainly concrete, cement or fly ash, coal gangue and other industrial waste and household garbage produced by the non-clay brick, building blocks and building boards and construction techniques, materials, technology, detection means there is no specification limit. This paper presents the using association rule mining to build the building wall materials system. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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.

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