International Journal of Data Mining Techniques and Applications最新文献_第9页

Weather Impact over Uttarakhand using k-Means Clustering Technique for Cloudburst Prediction 用k-均值聚类技术进行暴雨预报对北阿坎德邦的天气影响

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.010

D.R.N.Sravana Lakshmi, J. Karthikeyan

引用次数: 0

Image Segmentation Based Survey on the Lung Cancer MRI Images 基于图像分割的肺癌MRI图像调查

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.015

S. Perumal, T. Velmurugan

引用次数: 1

Genetic Based ID3 Classification Algorithm Diagnosis and Prognosis of Oral Cancer 基于遗传的ID3分类算法对口腔癌诊断及预后的影响

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.009

K. Jamberi, E. Ramaraj

{"title":"Genetic Based ID3 Classification Algorithm Diagnosis and Prognosis of Oral Cancer","authors":"K. Jamberi, E. Ramaraj","doi":"10.20894/IJDMTA.102.005.002.009","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.002.009","url":null,"abstract":": In order to analyse the chosen data from various points of view, data mining is used as the effective process. This process is also used to sum-up all those views into useful information. There are several types of algorithms in data mining such as Classification algorithms, Regression, Segmentation algorithms, association algorithms, sequence analysis algorithms, etc.,. The classification algorithm can be usedto bifurcate the data set from the given data set and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original data set S as the root node. An unutilised attribute of the data set S calculates the entropy H(S) (or Information gain IG (A)) of the attribute. Upon its selection, the attribute should have the smallest entropy (or largest information gain) value. A genetic algorithm (GA) is a heuristic quest that imitates the process of natural selection. Genetic algorithm can easily select cancer data set, from the given data set using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for oral cancer and primary tumor. Our method of creating new algorithm GA+ID3 easily identifiesoral cancer data set from the given data set. The genetic based ID3 classification algorithm diagnosis and prognosis of oral cancer data set is identified by this paper.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"16 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":"128273545","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

A Survey on Educational Data Mining Tools and Techniques 教育数据挖掘工具和技术综述

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.014

A. Arunachalam, T. Velmurugan

引用次数: 8

Leanness Assessment using Fuzzy Logic Approach: A Case of Indian Horn Manufacturing Company 模糊逻辑方法的精益评价——以印度牛角制造公司为例

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.001

P. Balasubramanian, K. Hemamala.

引用次数: 1

A Study on MRI Liver Image Segmentation using Fuzzy Connected and Watershed Techniques 基于模糊连接和分水岭技术的MRI肝脏图像分割研究

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.004

A. Thenmozhi, N. Radhakrishnan

{"title":"A Study on MRI Liver Image Segmentation using Fuzzy Connected and Watershed Techniques","authors":"A. Thenmozhi, N. Radhakrishnan","doi":"10.20894/IJDMTA.102.005.002.004","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.002.004","url":null,"abstract":"- A comparison study between automatic and interactive methods for liver segmentation from contrast-enhanced MRI images is ocean. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to refer five error measures that highlight different aspects of segmentation accuracy. The measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods like Fuzzy Connected and Watershed Methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. In this paper only Fuzzy Connected and Watershed Methods are discussed.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"22 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":"128183160","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

Market Basket Analysis using Improved FP-tree 基于改进fp树的购物篮分析

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.002

Abhishek Priyadarshi, Chirag Gupta, G. Poornalatha

{"title":"Market Basket Analysis using Improved FP-tree","authors":"Abhishek Priyadarshi, Chirag Gupta, G. Poornalatha","doi":"10.20894/IJDMTA.102.005.002.002","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.002.002","url":null,"abstract":"The Market Basket Analysis helps in identifying the \u0000purchasing patterns of customers such as, which products are purchased more and which products are purchased together. This helps in decision making process. For example, if two or more products are frequently purchased together then they can be kept at the same place so as to facilitate the customer, to further increase their sale. The price of products that are not frequently \u0000purchased can be reduced in order to enhance their purchase. Additionally the promotion of one product will also increase the sales of other products which are purchased together with the product being promoted. The traditional Apriori algorithm based on candidate generation cannot be used in Market Basket Analysis because it generates candidate sets and scans database regularly for the generation of frequent itemsets. The FP-growth algorithm cannot be used despite of the fact that it does not generate candidate sets and scans the database only twice because, it generates a lot of conditional trees recursively. Therefore, an efficient algorithm needs to be used. In this paper an efficient algorithm is used for development of market basket analysis application. This efficient algorithm neither generates candidate sets nor conditional FP- tree; like FP-growth scans the database twice","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"454 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":"117252411","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

A Review of Edge Detection Techniques for Image Segmentation 图像分割中的边缘检测技术综述

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.008

S. Jeyalaksshmi, S. Prasanna

引用次数: 0

Efficiency of k-Means and k-Medoids Clustering Algorithms using Lung Cancer Dataset 肺癌数据k-Means和k-Medoids聚类算法的效率

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.011

A. Dharmarajan, T. Velmurugan

{"title":"Efficiency of k-Means and k-Medoids Clustering Algorithms using Lung Cancer Dataset","authors":"A. Dharmarajan, T. Velmurugan","doi":"10.20894/IJDMTA.102.005.002.011","DOIUrl":"https://doi.org/10.20894/IJDMTA.102.005.002.011","url":null,"abstract":"The objective of this research work is focused on the right cluster creation of lung cancer data and analyzed the efficiency of k-Means and k-Medoids algorithms. This research work would help the developers to identify the characteristics and flow of algorithms. In this research work is pertinent for the department of oncology in cancer centers. This implementation helps the oncologist to make decision with lesser execution time of the algorithm.It is also enhances the medical care applications. This work is very suitable for selection of cluster development algorithm for lung cancer data analysis.Clustering is an important technique in data mining which is applied in many fields including medical diagnosis to find diseases. It is the process of grouping data, where grouping is recognized by discovering similarities between data based on their features. In this research work, the lung cancer data is used to find the performance of clustering algorithms via its computational time. Considering a limited number attributes of lung cancer data, the algorithmic steps are applied to get results and compare the performance of algorithms. The partition based clustering algorithms k-Means and k-Mediods are selected to analyze the lung cancer data.The efficiency of both the algorithms is analyzed based on the results produced by this approach. The finest outcome of the performance of the algorithm is reported for the chosen data concept.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"5 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":"129807805","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

Survey on Segmentation Techniques for Spinal Cord Images 脊髓图像分割技术综述

International Journal of Data Mining Techniques and Applications Pub Date : 2016-12-15 DOI: 10.20894/IJDMTA.102.005.002.005

S. Mary, S. Sasikala

引用次数: 1