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International Journal of Data Mining & Knowledge Management Process最新文献

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A Review On Evaluation Metrics For Data Classification Evaluations 数据分类评价指标综述
International Journal of Data Mining & Knowledge Management Process Pub Date : 2015-03-31 DOI: 10.5121/IJDKP.2015.5201
H. M., Sulaiman M.N
{"title":"A Review On Evaluation Metrics For Data Classification Evaluations","authors":"H. M., Sulaiman M.N","doi":"10.5121/IJDKP.2015.5201","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5201","url":null,"abstract":"Evaluation metric plays a critical role in achieving the optimal classifier during the classification training. \u0000Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the \u0000optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically \u0000designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers \u0000employ accuracy as a measure to discriminate the optimal solution during the classification training. \u0000However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less \u0000informativeness and bias to majority class data. This paper also briefly discusses other metrics that are \u0000specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics \u0000are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration \u0000in constructing a new discriminator metric.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115418579","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: 964
Effective Data Mining for Proper Mining Classification Using Neural Networks 基于神经网络的有效数据挖掘与分类
International Journal of Data Mining & Knowledge Management Process Pub Date : 2015-03-31 DOI: 10.5121/IJDKP.2015.5206
Gaurab Tewary
{"title":"Effective Data Mining for Proper Mining Classification Using Neural Networks","authors":"Gaurab Tewary","doi":"10.5121/IJDKP.2015.5206","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5206","url":null,"abstract":"With the development of database, the data volume stored in database increases rapidly and in the large amounts of data much important information is hidden. If the information can be extracted from the database they will create a lot of profit for the organization. The question they are asking is how to extract this value. The answer is data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128287626","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
A Novel Algorithm for Mining Closed Sequential Patterns 一种新的封闭序列模式挖掘算法
International Journal of Data Mining & Knowledge Management Process Pub Date : 2015-01-31 DOI: 10.5121/IJDKP.2015.5104
P. Raju, Saradhi Varma G.P
{"title":"A Novel Algorithm for Mining Closed Sequential Patterns","authors":"P. Raju, Saradhi Varma G.P","doi":"10.5121/IJDKP.2015.5104","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5104","url":null,"abstract":"Sequential pattern mining algorithms produce an exponential number of sequential patterns when mining long patterns or at low support thresholds. Most of the existing algorithms mine the full set of sequential patterns. However, it is sufficient to mine closed sequential patterns from which the total set of sequential patterns can be derived and the closed sequential patterns set is more compact than the sequential patterns set. In this paper, we propose a novel algorithm NCSP for mining closed sequential patterns in large sequences databases. To the best of our knowledge, our algorithm is the first algorithm that utilizes vertical bitmap representation for closed sequential pattern mining. The results show that the proposed algorithm NCSP can find closed sequential patterns efficiently and outperforms CloSpan by an order of magnitude.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930370","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: 2
Data mining in higher education: university student dropout case study 高等教育中的数据挖掘:大学生辍学案例研究
International Journal of Data Mining & Knowledge Management Process Pub Date : 2015-01-31 DOI: 10.5121/IJDKP.2015.5102
Ghadeer S. Abu-Oda, A. El-Halees
{"title":"Data mining in higher education: university student dropout case study","authors":"Ghadeer S. Abu-Oda, A. El-Halees","doi":"10.5121/IJDKP.2015.5102","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5102","url":null,"abstract":"In this paper, we apply different data mining approaches for the purpose of examining and predicting students’ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The collected data included student study history and transcript for courses taught in the first two years of computer science major in addition to student GPA , high school average , and class label of (yes ,No) to indicate whether the student graduated from the chosen major or not. In order to classify and predict dropout students, different classifiers have been trained on our data sets including Decision Tree (DT), Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships between student dropout status and enrolment persistence by mining a frequent cases using FP-growth algorithm.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122611630","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: 57
Data-Performance Characterization of Frequent Pattern Mining Algorithms 频繁模式挖掘算法的数据性能表征
International Journal of Data Mining & Knowledge Management Process Pub Date : 2015-01-31 DOI: 10.5121/IJDKP.2015.5105
Sayaka Akioka
{"title":"Data-Performance Characterization of Frequent Pattern Mining Algorithms","authors":"Sayaka Akioka","doi":"10.5121/IJDKP.2015.5105","DOIUrl":"https://doi.org/10.5121/IJDKP.2015.5105","url":null,"abstract":"Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely huge amount of data, it is quite natural that the demand for the computational environment to accelerates, and scales out big data applications increases. The important thing is, however, the behavior of big data applications is not clearly defined yet. Among big data applications, this paper specifically focuses on stream mining applications. The behavior of stream mining applications varies according to the characteristics of the input data. The parameters for data characterization are, however, not clearly defined yet, and there is no study investigating explicit relationships between the input data, and stream mining applications, either. Therefore, this paper picks up frequent pattern mining as one of the representative stream mining applications, and interprets the relationships between the characteristics of the input data, and behaviors of signature algorithms for frequent pattern mining.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122575305","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
Emotion Detection From Text Documents 从文本文档情感检测
International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-11-30 DOI: 10.5121/IJDKP.2014.4605
S. Shivhare, S. Saritha
{"title":"Emotion Detection From Text Documents","authors":"S. Shivhare, S. Saritha","doi":"10.5121/IJDKP.2014.4605","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4605","url":null,"abstract":"Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126418347","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: 5
Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review 基于概念类、概念漂移和缺失特征的非平衡数据增量学习综述
International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-11-30 DOI: 10.5121/IJDKP.2014.4602
P. Kulkarni, Roshani Ade
{"title":"Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review","authors":"P. Kulkarni, Roshani Ade","doi":"10.5121/IJDKP.2014.4602","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4602","url":null,"abstract":"Recently, stream data mining applications has drawn vital attention from several research communities. Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine learning area has been developing learning algorithms that have certain assumptions on underlying distribution of data such as data should have predetermined distribution. Such constraints on the problem domain lead the way for development of smart learning algorithms performance is theoretically verifiable. Real-word situations are different than this restricted model. Applications usually suffers from problems such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also usual in real world applications, resulting in the “concept drift” which is related with data stream examples. These issues have been separately addressed by the researchers, also, it is observed that joint problem of class imbalance and concept drift has got relatively little research. If the final objective of clever machine learning techniques is to be able to address a broad spectrum of real world applications, then the necessity for a universal framework for learning from and tailoring (adapting) to, environment where drift in concepts may occur and unbalanced data distribution is present can be hardly exaggerated. In this paper, we first present an overview of issues that are observed in stream data mining scenarios, followed by a complete review of recent research in dealing with each of the issue.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144632","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: 32
Arabic Words Stemming Approach Using Arabic Wordnet 基于阿拉伯语Wordnet的阿拉伯语词干提取方法
International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-11-30 DOI: 10.5121/IJDKP.2014.4601
Abdel Hamid Kreaa, Ahmad S Ahmad, Kassem Kabalan
{"title":"Arabic Words Stemming Approach Using Arabic Wordnet","authors":"Abdel Hamid Kreaa, Ahmad S Ahmad, Kassem Kabalan","doi":"10.5121/IJDKP.2014.4601","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4601","url":null,"abstract":"The big growth of the Arabic internet content in the last years has raised up the need for an effective stemming techniques for Arabic language. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja); stem-based approach (ex. Larkey); and statistical approach (ex. N-Garm). However, no stemming of this language is perfect: The existing stemmers have a low efficiency. In this paper, we introduce a new stemming technique for Arabic words that also solve the problem of the plural form of irregular nouns in Arabic language, which called broken plural. The proposed stem extractor provides very accurate results in comparisons with other algorithms. Consequently the search effectiveness improved.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"4 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121003873","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: 13
A Near-Duplicate Detection Algorithm to Facilitate Document Clustering 一种促进文档聚类的近重复检测算法
International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-11-30 DOI: 10.5121/IJDKP.2014.4604
Lavanya Pamulaparty, D. C. V. G. Rao, D. S. Rao
{"title":"A Near-Duplicate Detection Algorithm to Facilitate Document Clustering","authors":"Lavanya Pamulaparty, D. C. V. G. Rao, D. S. Rao","doi":"10.5121/IJDKP.2014.4604","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4604","url":null,"abstract":"Web Ming faces huge problems due to Duplicate and Near Duplicate Web pages. Detecting Near Duplicates is very difficult in large collection of data like ”internet”. The presence of these web pages plays an important role in the performance degradation while integrating data from heterogeneous sources. These pages either increase the index storage space or increase the serving costs. Detecting these pages has many potential applications for example may indicate plagiarism or copyright infringement. This paper concerns detecting, and optionally removing duplicate and near duplicate documents which are used to perform clustering of documents .We demonstrated our approach in web news articles domain. The experimental results show that our algorithm outperforms in terms of similarity measures. The near duplicate and duplicate document identification has resulted reduced memory in repositories.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576401","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
AN EXPERIMENTAL STUDY ON HYPOTHYROID USING ROTATION FOREST 轮作林治疗甲状腺功能减退的试验研究
International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-11-30 DOI: 10.5121/IJDKP.2014.4603
Sheetal Gaikwad, N. Pise
{"title":"AN EXPERIMENTAL STUDY ON HYPOTHYROID USING ROTATION FOREST","authors":"Sheetal Gaikwad, N. Pise","doi":"10.5121/IJDKP.2014.4603","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4603","url":null,"abstract":"This paper majorly focuses on hypothyroid medical diseases caused by underactive thyroid glands. The dataset used for the study on hypothyroid is taken from UCI repository. Classification of this thyroid disease is a considerable task. An experimental study is carried out using rotation forest using features selection methods to achieve better accuracy. An important step to gain good accuracy is a pre- processing step, thus here two feature selection techniques are used. A filter method, Correlation features subset selection and wrappers method has helped in removing irrelevant as well as useless features from the data set. Fourteen different machine learning algorithms were tested on hypothyroid data set using rotation forest which successfully turned out giving positively improved results.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123605922","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: 10
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