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International Journal of Data Mining Techniques and Applications最新文献

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An Efficient Logical Average Distance Measure Algorithm (LADMA) to Analyse MRI Brain Images 一种有效的逻辑平均距离测量算法(LADMA)分析MRI脑图像
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.001
A.Naveen Mr., T.Velmurugan Dr
{"title":"An Efficient Logical Average Distance Measure Algorithm (LADMA) to Analyse MRI Brain Images","authors":"A.Naveen Mr., T.Velmurugan Dr","doi":"10.20894/ijdmta.102.008.001.001","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.001","url":null,"abstract":"Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. Some of such data mining technique is k means and fuzzy C means algorithms. This work proposes a new hybrid algorithm namely LAMDA, which offers successful identification of tumor and perform well for the segmentation of tissue regions in brain. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. Experimental results show that the proposed approach reduces the number of features and at the same time it achieves high accuracy level. The observed results to achieve high accuracy level using minimum number of selected features.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348087","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 Fuzzy Clustering Based Approach for Heavy Tail Weight in Web Data 基于模糊聚类的Web数据重尾权处理方法
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.009
Janani K Ms
{"title":"A Fuzzy Clustering Based Approach for Heavy Tail Weight in Web Data","authors":"Janani K Ms","doi":"10.20894/ijdmta.102.008.001.009","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.009","url":null,"abstract":"A distributed frameworks cluster is a gathering of machines that are for all intents and purposes or topographically isolated and that cooperate to give a similar services or application to customers in web application. It is conceivable that huge numbers of the services you keep running in your system today are a piece of a distributed system cluster. Cluster is a vital information mining procedure which expects to isolate the information objects into important gatherings called as groups. It is the way toward gathering objects into bunches to such an extent that items from a similar group are comparable and objects from various groups is unique. In information mining, information bunching has been examined for long time utilizing diverse calculations and ordinary patterns are proposed for better results around tailed data. The fuzzy semantic strategy is look at to group the overwhelming followed information by utilizing some technique for remove. An appraisal thinks about is introduced in view of time and exactness. In this method proposed here is evaluated to other relational clustering schemes using various propinquity matrices as input. Simulations demonstrate the scheme to be very effectual.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121467133","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
Relevance of Reaction Surface Strategy and Artificial Neural Network Proposal in Representing and Development of Confession-Suction Procedure 反应面策略与人工神经网络方案在招供程序表现与发展中的相关性
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.004
M. Masillamani, Chamberlain Mr
{"title":"Relevance of Reaction Surface Strategy and Artificial Neural Network Proposal in Representing and Development of Confession-Suction Procedure","authors":"M. Masillamani, Chamberlain Mr","doi":"10.20894/ijdmta.102.008.001.004","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.004","url":null,"abstract":"A survey on the use of reaction surface strategy (RSS) and Artificial neural network (ANN) in confession-suction demonstrating and improvement is displayed. The hypothetical foundation of the examined strategies with the application strategy is clarified. The paper portrays most every now and again utilized trial outlines, concerning their constraints and normal applications. The paper additionally exhibits approaches to decide the precision and the hugeness of model fitting for the two strategies depicted in this. Moreover, late references on confession-suction demonstrating and advancement with the utilization of RSS and the ANN approach are appeared. Uncommon consideration was paid to the choice of variables and reactions, and in addition to factual examination of the displaying comes about.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"101 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132624804","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
The Review on About the Mahatma Gandhi NREGA Analysis using Data Mining Techniques 基于数据挖掘技术的圣雄甘地NREGA分析综述
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.010
John Bernard Z
{"title":"The Review on About the Mahatma Gandhi NREGA Analysis using Data Mining Techniques","authors":"John Bernard Z","doi":"10.20894/ijdmta.102.008.001.010","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.010","url":null,"abstract":"The Indian government is familiarizing many schemes of the encouragement of poor but they are not embracing them in a felicitous and convenient path. There is ambivalence or inadequacy scrutinize in materialized schemes. The system of MGNREGA is one of the expedient materialized by the governance of India in preservation sense that �The spirit Of India Lives in Its rural community�, uttermost of the populace in the imagined place of rural bliss part of the country is gamble on the tongue-tied sweat of one�s for their endurance. MGNREGA is one of such schemes materialized by the government of India which indent at providing hobby to the poor in rural areas by assigning local work them. Data mining tactics that is categorization, bigotry, classification, clustering, outlier and tendency analysis etc...Are enforced on various district for awkward labium, material and juncture are collected separately and presented in this paper and also analysis of the payment of wages to the workers under MGNREGA scheme.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128986542","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
The Study on Clustering Analysis in Data Mining 数据挖掘中的聚类分析研究
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.011
Scott Mr, A. Caroline
{"title":"The Study on Clustering Analysis in Data Mining","authors":"Scott Mr, A. Caroline","doi":"10.20894/ijdmta.102.008.001.011","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.011","url":null,"abstract":"Cluster analysis is the duty of assemblage a set of items in such a manner that items in the same group are more alike to each other than to those in other groups .A collection of data entities can be treated as one group. Whereas undertaking gathering investigation, we first distinct the regular of records into groups based on data association and then assign the tags to the groups. The main advantage of gathering over arrangement is that, it is adaptable to variations and helps single out useful features that distinguishes dissimilar groups. It is a most significant tasks to efficient the data mining, and a common method for numerical data analysis, used in numerous fields. In This paper converse about different types of clustering algorithms such as Partitioning Method, Hierarchical Method Density-based Method, Grid-Based Method, Model-Based Method, and Constraint-based Method.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919094","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 Recent Trends and Outcomes in Data Science 数据科学的最新趋势和结果调查
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.006
Srijon Sen
{"title":"A Survey on Recent Trends and Outcomes in Data Science","authors":"Srijon Sen","doi":"10.20894/ijdmta.102.008.001.006","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.006","url":null,"abstract":"The current era in science, computing, and technology are seeing a lot of movements and revolutions taking place around data science. Data Science is a booming market and lots of research and effort is being made to its outcomes. Now, to make out what exactly data science is then simply a layman would say, the science that revolves around data or the science that deals with gaining insights from data. Data science is only useful when the data is used to answer a specific or a concrete set of question. This research work mainly focuses on the recent data science hot topics and trends that will benefit the community in the long run and what all things that are achievable through data science.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132211191","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
The Review on Data Mining Techniques and its Applications 数据挖掘技术及其应用综述
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.012
C. Ms, M. Parimala
{"title":"The Review on Data Mining Techniques and its Applications","authors":"C. Ms, M. Parimala","doi":"10.20894/ijdmta.102.008.001.012","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.012","url":null,"abstract":"Data mining is a field of research which is escalating gradually. Data mining is the method of analyze data from unique views and summarization it interested in useful data. Data mining, also in general referred to like knowledge discovery from data (KDD), is the computerize or suitable extraction of patterns in place of knowledge completely stored or captured in large databases, data warehouse, the web, other vast information repositories or information streams. Data mining machine works with data warehouse and the whole process is divided into action plan to be performed on data: selection, transformation, mining and results elucidation. In this term paper, we have reviewed equal types of obedience in data mining, also make clear different areas somewhere used data mining feeling and used of it","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121569249","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
APRM to Isolate Behavior (Frequent or Infrequent) by using Cross-Organizational Process Mining APRM通过使用跨组织过程挖掘来隔离行为(频繁或不频繁)
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.007
Pavithra. J Ms
{"title":"APRM to Isolate Behavior (Frequent or Infrequent) by using Cross-Organizational Process Mining","authors":"Pavithra. J Ms","doi":"10.20894/ijdmta.102.008.001.007","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.007","url":null,"abstract":"Process mining is a generally youthful and creating research zone with the primary thought of finding, checking and enhancing forms by removing data from occasion logs. Going out on a limb viewpoint on the business procedure administration (BPM) lifecycle has in this manner been perceived as a fundamental research stream. Notwithstanding significant information on hazard mindful BPM with an attention on process configuration, existing methodologies for real time chance observing regard occurrences as confined when identifying dangers. To address this hole, we propose an approach for Anomaly Predictive - Risk Monitoring (APRM). This approach naturally spreads chance data, which has been identified by means of hazard sensors, crosswise over comparable running occasions of a similar procedure progressively. We show APRMs capacity of prescient hazard checking by applying it with regards to a certifiable situation. With the expansion of distributed computing and shared foundations, occasion logs of various associations are accessible for examination where cross-hierarchical process mining remains with the open door for associations gaining from each other. Created proposal comes about demonstrate that the utilization of this approach can help clients to concentrate on the parts of process models with potential execution change, which are hard to spot physically and outwardly.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129506767","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
Improve Manufacturing Industry Sentimental Analysis for Machine Learning 改进制造业情感分析的机器学习
International Journal of Data Mining Techniques and Applications Pub Date : 2019-06-05 DOI: 10.20894/ijdmta.102.008.001.008
Ponniammal M Ms
{"title":"Improve Manufacturing Industry Sentimental Analysis for Machine Learning","authors":"Ponniammal M Ms","doi":"10.20894/ijdmta.102.008.001.008","DOIUrl":"https://doi.org/10.20894/ijdmta.102.008.001.008","url":null,"abstract":"High competitive pressure in the global manufacturing industry makes a successful, successful and sustainable development processes a successful factor. However, analyzes produced by the production control systems are defined by continuous functional improvements defined by large short-rumors. In particular, they do not use data mining to identify hidden patterns in product data. The data mining approach offered by the Advanced Production Analytics Platform is currently based on identification and method based production process optimization. Twitter is a big fast growing micro-blogging social networking platform to express their opinions about the constitution and products game. These ideas are useful for businesses, government and individuals. Therefore, tweets can be used as a valuable source for public opinion. Emotional analysis is a process to automatically identify whether a user-generated speech expresses a positive, negative or neutral point of view of a company.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731512","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
Multilevel Classification Algorithm using Diagnosis and Prognosis of Breast Cancer 应用于乳腺癌诊断和预后的多级分类算法
International Journal of Data Mining Techniques and Applications Pub Date : 2018-12-26 DOI: 10.20894/ijdmta.102.007.002.012
Kviecinski Mr, Eccles Mr
{"title":"Multilevel Classification Algorithm using Diagnosis and Prognosis of Breast Cancer","authors":"Kviecinski Mr, Eccles Mr","doi":"10.20894/ijdmta.102.007.002.012","DOIUrl":"https://doi.org/10.20894/ijdmta.102.007.002.012","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 used to bifurcate the affected image from the given affected image and foretell one or more discrete variables, based on the other attributes in the dataset. The ID3 (Iterative Dichotomiser 3) algorithm is an original affected image S as the root node. An unutilised attribute of the affected image 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 affected image using GA operators, such as mutation, selection, and crossover. A method existed earlier (KNN+GA) was not successful for breast cancer and primary tumor. Our method of creating new algorithm GA and decision tree algorithm easily identifies breast cancer affected image. The genetic based classification algorithm diagnosis and prognosis of breast cancer affected is identified by this paper.","PeriodicalId":414709,"journal":{"name":"International Journal of Data Mining Techniques and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122816270","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
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