International Journal of Data Mining & Knowledge Management Process最新文献_第8页

FINDING FREQUENT SUBPATHS IN A G RAPH 查找图中频繁的子路径

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-09-30 DOI: 10.5121/IJDKP.2014.4503

S. Guha, Klong Luang

引用次数: 2

Students Performance Prediction System Using Multi Agent Data Mining Technique 基于多智能体数据挖掘技术的学生成绩预测系统

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-09-30 DOI: 10.5121/IJDKP.2014.4501

Abdullah Al-Malaise, A. Malibari, Mona Alkhozae

{"title":"Students Performance Prediction System Using Multi Agent Data Mining Technique","authors":"Abdullah Al-Malaise, A. Malibari, Mona Alkhozae","doi":"10.5121/IJDKP.2014.4501","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4501","url":null,"abstract":"A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making. Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for the binary classification and not applicable to multiclass classification directly. SAMME boosting technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binary classification. In this paper, students’ performance prediction system using Multi Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide help to the low students by optimization rules. The proposed system has been implemented and evaluated by investigate the prediction accuracy of Adaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed C4.5 single classifier and LogitBoost.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133386682","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: 23

Data Mining in Education : A Review on the Knowledge Discovery Perspective 教育中的数据挖掘:知识发现视角述评

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-09-30 DOI: 10.5121/IJDKP.2014.4504

P. Guleria, M. Sood

引用次数: 21

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering" “模糊K-Means优于K-Means聚类”假设的实验

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-09-30 DOI: 10.5121/IJDKP.2014.4502

Srinivas Sivarathri, A. Govardhan

{"title":"Experiments on Hypothesis \"Fuzzy K-Means is Better than K-Means for Clustering\"","authors":"Srinivas Sivarathri, A. Govardhan","doi":"10.5121/IJDKP.2014.4502","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4502","url":null,"abstract":"Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364443","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: 20

The Evaluation of Factors Influencing Safety Performance : A Case in an Industrial Gas Manufacturing Company (Ghana) 安全绩效影响因素评价——以加纳某工业气体制造公司为例

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-09-30 DOI: 10.5121/IJDKP.2014.4505

Evelyn Enchill, K. Mireku

引用次数: 3

An Efficient Algorithm for Privacy Preserving Data Mining Using Hybrid Transformation 基于混合变换的高效隐私保护数据挖掘算法

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-07-31 DOI: 10.5121/IJDKP.2014.4404

H. Jalla, Girija P.N

引用次数: 7

A Fuzzy Logic Based Sentiment Classification 基于模糊逻辑的情感分类

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-07-31 DOI: 10.5121/IJDKP.2014.4403

Sheeba J.I., K. Vivekanandan

{"title":"A Fuzzy Logic Based Sentiment Classification","authors":"Sheeba J.I., K. Vivekanandan","doi":"10.5121/IJDKP.2014.4403","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4403","url":null,"abstract":"Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add some additional features for improving the classification method. The quality of the sentiment classification is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 % accurate results and error rate is very less compared to existing sentiment classification techniques.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121969043","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: 11

Application of Data Mining Tools for Selected Scripts of Stock Market 数据挖掘工具在股票市场精选脚本中的应用

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-07-31 DOI: 10.5121/IJDKP.2014.4405

Mahajan K.S, K. R.V

{"title":"Application of Data Mining Tools for Selected Scripts of Stock Market","authors":"Mahajan K.S, K. R.V","doi":"10.5121/IJDKP.2014.4405","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4405","url":null,"abstract":"One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions Therefore, Investment can be considered as one of the fundamental pillars of national economy. So, at the present time many investors look to find criterion to compare stocks together and selecting the best and also investors choose strategies that maximize the earning value of the investment process. Therefore the enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Therefore research in data mining has gained a high attraction due to the importance of its applications and the increasing generation information. So, Data mining tools such as association rule, rule induction method and Apriori algorithm techniques are used to find association between different scripts of stock market, and also much of the research and development has taken place regarding the reasons for fluctuating Indian stock exchange. But, now days there are two important factors such as gold prices and US Dollar Prices are more dominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices and BSE index statistical correlation is used and this helps the activities of stock operators, brokers, investors and jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollar prices and transactions of customers. Hence researcher has considered these problems as a topic for research.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"167 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120979832","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: 1

Stratification of Clinical Survey Data by Using Contingency Tables 应用列联表对临床调查数据进行分层

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-07-31 DOI: 10.5121/IJDKP.2014.4401

S. Arslanturk, Mohammad-Reza Siadat, T. Ogunyemi, B. Givens, A. Diokno

引用次数: 1

Additive Gaussian Noise Based Data Perturbation in Multi-Level Trust Privacy Preserving Data Mining 基于加性高斯噪声的多级信任隐私保护数据挖掘数据扰动

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-05-30 DOI: 10.5121/IJDKP.2014.4303

Kalaivani R, Chidambaram S

引用次数: 5