International Journal of Data Mining Techniques and Applications最新文献_第10页

An approach for Breast Cancer classification using Neural Networks 基于神经网络的乳腺癌分类方法

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.003

D. Gladis, S. Vijaya

引用次数: 0

Comparative Analysis of Weighted Emphirical Optimization Algorithm and Lazy Classification Algorithms 加权经验优化算法与惰性分类算法的比较分析

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.006

P. Suganya, C. Sumathi

引用次数: 0

Performance Evaluation of Feature Selection Algorithms in Educational Data Mining 教育数据挖掘中特征选择算法的性能评价

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.007

C. Anuradha, T. Velmurugan

{"title":"Performance Evaluation of Feature Selection Algorithms in Educational Data Mining","authors":"C. Anuradha, T. Velmurugan","doi":"10.20894/IJDMTA.102.005.002.007","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.002.007","url":null,"abstract":": Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be high than other classifiers over the CFS subset evaluator. Also found that overall accuracy of CFS subset evaluator seems to be high than other feature selection algorithms. The future work will concentrate on the implementation of a proposed hybrid method by considering large dataset collected from many institutions.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127770743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Enabling Cloud Storage Auditing with Key Exposure Resistance 启用具有密钥暴露阻力的云存储审计

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.017

S. Santhiya, R. Arun

{"title":"Enabling Cloud Storage Auditing with Key Exposure Resistance","authors":"S. Santhiya, R. Arun","doi":"10.20894/IJDMTA.102.005.001.017","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.001.017","url":null,"abstract":"Abstract With cloud computing, users can remotely store their data into the cloud and use on-demand high-quality applications. Data outsourcing: users are relieved from the burden of data storage and maintenance When users put their data (of large size) on the cloud, the data integrity protection is challenging enabling public audit for cloud data storage security is important Users can ask an external audit party to check the integrity of their outsourced data. Purpose of developing data security for data possession at un-trusted cloud storage servers we are often limited by the resources at the cloud server as well as at the client. Given that the data sizes are large and are stored at remote servers, accessing the entire file can be expensive in input output costs to the storage server. Also transmitting the file across the network to the client can consume heavy bandwidths. Since growth in storage capacity has far outpaced the growth in data access as well as network bandwidth, accessing and transmitting the entire archive even occasionally greatly limits the scalability of the network resources. Furthermore, the input output to establish the data proof interferes with the on-demand bandwidth of the server used for normal storage and retrieving purpose. The Third Party Auditor is a respective person to manage the remote data in a global manner.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121010377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectrum Management Techniques using Cognitive Radios Cognitive Radio Technology 使用认知无线电的频谱管理技术

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.019

U. Arul, S. Rajan

{"title":"Spectrum Management Techniques using Cognitive Radios Cognitive Radio Technology","authors":"U. Arul, S. Rajan","doi":"10.20894/IJDMTA.102.005.001.019","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.001.019","url":null,"abstract":"Spectrum has been a very valuable resource in wireless communication systems. The available electromagnetic radio spectrum is getting crowded day by day due to manipulation in wireless devices and applications. Underutilization of Spectrum has become a major source of concern for each network user. The present paper attempts to portray “Spectrum Management Techniques using Cognitive Radios”, where the strength and scope of Cognitive Radio Technology are discussed. It also highlights the efficiency and effectiveness of the system when compared to conventional mode of operations. Further, the present paper also lucidly explains the modus operandus of Cognitive Radio Technology Spectrum Management Techniques namely Spectrum Sensing, Spectrum Decision-Making, Spectrum Sharing and Spectrum Mobility. These functionalities make Cognitive Radio Technology an asset to the network domain and easily solves issues like interference, noise and underutilization. The paper also focusses on describing the Transreceiver and network architecture. On the whole, this paper is an overall description about the Spectrum Management Techniques in Cognitive Radio Technology in brief.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123922734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Service Level Comparison for Online Shopping using Data Mining 基于数据挖掘的网上购物服务水平比较

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.005

K. Chandini, A. Roshini, A. Kokila, B. Aishwarya

{"title":"Service Level Comparison for Online Shopping using Data Mining","authors":"K. Chandini, A. Roshini, A. Kokila, B. Aishwarya","doi":"10.20894/IJDMTA.102.005.001.005","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.001.005","url":null,"abstract":"The term knowledge discovery in databases (KDD) is the analysis step of data mining. The data mining goal is to extract the knowledge and patterns from large data sets, not the data extraction itself. Big-Data Computing is a critical challenge for the ICT industry. Engineers and researchers are dealing with the cloud computing paradigm of petabyte data sets. Thus the demand for building a service stack to distribute, manage and process massive data sets has risen drastically. We investigate the problem for a single source node to broadcast the big chunk of data sets to a set of nodes to minimize the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed data centers. The Big-data broadcasting problem is modeled into a LockStep Broadcast Tree (LSBT) problem. And the main idea of the LSBT is defining a basic unit of upload bandwidth, r, a node with capacity c broadcasts data to a set of [c=r] children at the rate r. Note that r is a parameter to be optimized as part of the LSBT problem. The broadcast data are further divided into m chunks. In a pipeline manner, these m chunks can then be broadcast down the LSBT. In a homogeneous network environment in which each node has the same upload capacity c, the optimal uplink rate r, of LSBT is either c=2 or 3, whichever gives the smaller maximum completion time. For heterogeneous environments, an O(nlog2n) algorithm is presented to select an optimal uplink rate r, and to construct an optimal LSBT. With lower computational complexity and low maximum completion time, the numerical results shows better performance.The methodology includes Various Web applications Building and Broadcasting followed by the Gateway Application and Batch Processing over the TSV Data after which the Web Crawling for Resources and MapReduce process takes place and finally Picking Products from Recommendations and Purchasing it.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Implementation of Application Software for User Friendly Operation of Industrial Robot 工业机器人友好操作应用软件的设计与实现

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.012

S. Anitha, K. Joshitha

引用次数: 0

A Survey on Search Engine Optimization using Page Ranking Algorithms 基于页面排名算法的搜索引擎优化研究综述

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.009

M. S. Parveen, T. Nandhini, B. Kalpana

引用次数: 1

Sales Management Using Apriori Algorithm On SAP Fiori 基于SAP Fiori的Apriori算法销售管理

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.004

Ketki Kulkarni, Ashwini Ghuge, Priyanka Waghmare, Aishwarya Mali

{"title":"Sales Management Using Apriori Algorithm On SAP Fiori","authors":"Ketki Kulkarni, Ashwini Ghuge, Priyanka Waghmare, Aishwarya Mali","doi":"10.20894/IJDMTA.102.005.001.004","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.001.004","url":null,"abstract":"Sales management is an important aspect of all Business analytics outfits. However, in today’s digital world we need to find on-the-go solutions. We can find these by targeting the mobile Apps. We find that there are numerous mobile solutions offered by android Apps in the market. They are so popular because of compatibility on various mobile platforms. But a major problem faced by many outfits today is their compatibility with backend systems in use, namely the ERPs like SAP. In this paper we aim to solve this problem. We also portray a successful use of the upcoming SAP Fiori technology which works on multiple mobile platforms. Another major issue is business analytics on such mobile platforms. Using SAP ERP and the latest HANA databases, we can perform robust data mining algorithms on vast amounts of data. Infact, some datamining essential algorithms are already a part of such systems in the form of libraries. However, these databases are expensive and are unsuitable for small outfits using SAP services. Today, there exists virtually no library for data-mining on mobile devices without a Hana database. In our experiments we aim to solve this issue. We have created a market basket-analysis system to be deployed as a mobile App on SAP Fiori from scratch without a Hana database. We implemented an Apriori algorithm, to generate rules. We found that such a system can be developed with said requirements and has very low operational delay Keywords-Apriori algorithm, SAP Fiori, Sales management, ERP, Business analytics","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130885452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mining and Clustering the Feature Similarities of Images on Smart Phone 智能手机图像特征相似性的挖掘与聚类

International Journal of Data Mining Techniques and Applications Pub Date : 2016-06-15 DOI: 10.20894/IJDMTA.102.005.001.014

M. Samiya, S. Sharmiladevi, K. Surya, R. Sudha

{"title":"Mining and Clustering the Feature Similarities of Images on Smart Phone","authors":"M. Samiya, S. Sharmiladevi, K. Surya, R. Sudha","doi":"10.20894/IJDMTA.102.005.001.014","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.001.014","url":null,"abstract":"With the fame of visual sensor on smart phone devices, ( i.e. camera) it becomes a habit for many people to capture photos everyday and everywhere. This led to the rapid developing of more personal images and becomes a nuisance to the users in storing and organizing them, which had not been used before. Lu c k i l y , cloud storage provided a comprehensive solution at the right moment, and it facilitates the synchronization and sharing of images acquired. However, organizing this bulk number of personal images is still a tedious and difficult task. Common needs in photo organization may involve tagging, destroying replicated or same images, and collecting photos into albums. In our proposed system, we target to provide a features similarity images, face detection and recognition, avoid redundancy on smart mobile application which makes use of existing sensors and related technologies to help users to manage replicate or same images more effectively. By sharpening the power of cloud computing for SSIM algorithm, our system significantly reduce the time spent on managing photos in a neat and simple way which reduce user stress and increase user experience. KeywordsPhoto organization, Image similarity, Cloud storage, Mobile application, Redundancy avoidance.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114597737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0