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

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Building a Classifier Employing Prism Algorithm with Fuzzy Logic 基于模糊逻辑的棱镜算法构建分类器
International Journal of Data Mining & Knowledge Management Process Pub Date : 2017-03-31 DOI: 10.5121/IJDKP.2017.7204
Ishrat Nahar Farhana, Sajedul Hoque A.H.M, Rashed Mustafa, M. S. Chowdhury
{"title":"Building a Classifier Employing Prism Algorithm with Fuzzy Logic","authors":"Ishrat Nahar Farhana, Sajedul Hoque A.H.M, Rashed Mustafa, M. S. Chowdhury","doi":"10.5121/IJDKP.2017.7204","DOIUrl":"https://doi.org/10.5121/IJDKP.2017.7204","url":null,"abstract":"Classification in data mining is receiving immense interest in recent times. As the knowledge is based on historical data, classifications of data are essential for discovering the knowledge. To decrease the classification complexity, the quantitative attributes of data need splitting. But the splitting using the classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism decision tree algorithm. This classifier produces more realistic results than the classical one. The effectiveness of this method is justified over a sample dataset.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211486","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
Scalable Local Community Detection with Mapreduce for Large Networks Mapreduce用于大型网络的可扩展本地社区检测
International Journal of Data Mining & Knowledge Management Process Pub Date : 2017-03-31 DOI: 10.5121/IJDKP.2017.7203
Ren Wang, Andong Wang, Talat Syed, Osmar R Zaiane
{"title":"Scalable Local Community Detection with Mapreduce for Large Networks","authors":"Ren Wang, Andong Wang, Talat Syed, Osmar R Zaiane","doi":"10.5121/IJDKP.2017.7203","DOIUrl":"https://doi.org/10.5121/IJDKP.2017.7203","url":null,"abstract":"Community detection from complex information networks draws much attention from both academia and industry since it has many real-world applications. However, scalability of community detection algorithms over very large networks has been a major challenge. Real-world graph structures are often complicated accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that parallelizes a local community identification method which uses the $M$ metric. Then we adopt an iterative expansion approach to find all the communities in the graph. Empirical results show that for large networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional sequential approach to detect communities using the M-measure. The result shows that for local community detection, when the data is too big for the original M metric-based sequential iterative expension approach to handle, our MapReduce version 3MA can finish in a reasonable time.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131858877","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
Statistical Markovian Data Modeling for Natural Language Processing 自然语言处理中的统计马尔可夫数据建模
International Journal of Data Mining & Knowledge Management Process Pub Date : 2017-01-30 DOI: 10.5121/IJDKP.2017.7103
Fawaz S. Al-Anzi, DiaAbuZeina
{"title":"Statistical Markovian Data Modeling for Natural Language Processing","authors":"Fawaz S. Al-Anzi, DiaAbuZeina","doi":"10.5121/IJDKP.2017.7103","DOIUrl":"https://doi.org/10.5121/IJDKP.2017.7103","url":null,"abstract":"Markov chain theory is a popular statistical tool in applied probability that is quite useful in modelling real-world computing applications. Over the past years; there has been grown interest to employ Markov chain theory in statistical learning of temporal (i.e. time series) data. A wide range of applications found to utilize Markov concepts; such applications include computational linguists, image processing, communications, bioinformatics, finance systems, etc .In fact, Markov processes based research applied with great success in many of the most efficient natural language processing (NLP) tools. Hence, this paper explores the Markov chain theory and its extension hidden Markov models (HMM) in (NLP) applications. This paper also presents some aspects related to Markov chains and HMM such as creating transition and observation matrices, calculating data sequence probabilities, extracting the hidden states, and profile","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374091","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: 4
Terrorist Watcher : An Interactive Web Based Visual Analytical Tool of Terrorist's Personal Characteristics 恐怖分子观察者:基于交互式网络的恐怖分子个人特征可视化分析工具
International Journal of Data Mining & Knowledge Management Process Pub Date : 2017-01-30 DOI: 10.5121/IJDKP.2017.7101
Samah Mansoour
{"title":"Terrorist Watcher : An Interactive Web Based Visual Analytical Tool of Terrorist's Personal Characteristics","authors":"Samah Mansoour","doi":"10.5121/IJDKP.2017.7101","DOIUrl":"https://doi.org/10.5121/IJDKP.2017.7101","url":null,"abstract":"Terrorism is a phenomenon that rose to its peak nowadays. Counter terrorism analysts work with a large set of documents related to different terrorist groups and attack types to extract useful information about these groups’ motive and tactics. It is evident that terrorism became a global threat and can exist anywhere. In order to face this phenomenon, there is a need to understand the characteristics of the terrorists in order to find if there are general characteristics among all of them or not. However, as the number of the collected documents increase, deducing results and making decisions became more and more difficult to the analysts. The use of information visualization tools can help the analysts to visualize the terrorist characteristics. However, most of the current information visualization tools focus only on representing and analyzing the terrorist organizations, with little emphasis on terrorist’s personal characteristics. Therefore, the current paper presents a visualization tool that can be used to analyze the terrorist’s personal characteristics in order to understand the production life cycle of a terrorist and how to face it.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939090","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
Pattern Discovery for Multiple Data Sources Based on Item Rank 基于项目等级的多数据源模式发现
International Journal of Data Mining & Knowledge Management Process Pub Date : 2017-01-30 DOI: 10.5121/IJDKP.2017.7104
A. Deshpande, A. Mahajan, Thomas A
{"title":"Pattern Discovery for Multiple Data Sources Based on Item Rank","authors":"A. Deshpande, A. Mahajan, Thomas A","doi":"10.5121/IJDKP.2017.7104","DOIUrl":"https://doi.org/10.5121/IJDKP.2017.7104","url":null,"abstract":"","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128728613","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
Constructing a Text-Mining Based English Vocabulary Learning List - A Case Study of College Entrance Examination in Taiwan 基于文本挖掘的英语词汇学习表构建——以台湾高考为例
International Journal of Data Mining & Knowledge Management Process Pub Date : 2016-11-30 DOI: 10.5121/IJDKP.2016.6604
Yi-Ning Tu, Yu-Fang Lin, Jou-Cuei Chan
{"title":"Constructing a Text-Mining Based English Vocabulary Learning List - A Case Study of College Entrance Examination in Taiwan","authors":"Yi-Ning Tu, Yu-Fang Lin, Jou-Cuei Chan","doi":"10.5121/IJDKP.2016.6604","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6604","url":null,"abstract":"","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121846960","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
MR - Random Forest Algorithm for Distributed Action Rules Discovery MR -分布式动作规则发现的随机森林算法
International Journal of Data Mining & Knowledge Management Process Pub Date : 2016-09-30 DOI: 10.5121/IJDKP.2016.6502
A. Tzacheva, A. Bagavathi, Punniya D. Ganesan
{"title":"MR - Random Forest Algorithm for Distributed Action Rules Discovery","authors":"A. Tzacheva, A. Bagavathi, Punniya D. Ganesan","doi":"10.5121/IJDKP.2016.6502","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6502","url":null,"abstract":"Action rules, which are the modified versions of classification rules, are one of the modern approaches for discovering knowledge in databases. Action rules allow us to discover actionable knowledge from large datasets. Classification rules are tailored to predict the object’s class. Whereas action rules extracted from an information system produce knowledge in the form of suggestions of how an object can change from one class to another more desirable class. Over the years, computer storage has become larger and also the internet has become faster. Hence the digital data is widely spread around the world and even it is growing in size such a way that it requires more time and space to collect and analyze them than a single computer can handle. To produce action rules from a distributed massive data requires a distributed action rules processing algorithm which can process the datasets in all systems in one or more clusters simultaneously and combine them efficiently to induce single set of action rules. There has been little research on action rules discovery in the distributed environment, which presents a challenge. In this paper, we propose a new algorithm called MR – Random Forest Algorithm to extract the action rules in a distributed processing environment.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129754899","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
Using Ontology Based Semantic Association Rule Mining in Location Based Services 基于本体的语义关联规则挖掘在位置服务中的应用
International Journal of Data Mining & Knowledge Management Process Pub Date : 2016-09-30 DOI: 10.5121/IJDKP.2016.6501
A. Mousavi, A. Hunter, M. Akbari
{"title":"Using Ontology Based Semantic Association Rule Mining in Location Based Services","authors":"A. Mousavi, A. Hunter, M. Akbari","doi":"10.5121/IJDKP.2016.6501","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6501","url":null,"abstract":"Recently, GPS and mobile devices allowed collecting a huge amount of mobility data. Researchers from different communities have developed models and techniques for mobility analysis. But they mainly focused on the geometric properties of trajectories and do not consider the semantic facet of moving objects. The techniques are good at extracting patterns, but they are hard to interpret in a specific application domain. This paper proposes a methodology to understand mobility data and semantically interpret trajectory patterns. The process considers four different behavior types such as semantic, semantic and space, semantic and time, and semantic and space-time. Finally, a system prototype was developed to evaluate the behavior models in different aspects using one of the location based services. The results showed that applying the semantic association rules could significantly reduce the number of available services and customize the services based on the rules.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130005383","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: 4
Big Data-Driven Fast Reducing the Visual Block Artifacts of DCT Compressed Images for Urban Surveillance Systems 大数据驱动下快速降低城市监控系统DCT压缩图像的视觉块伪影
International Journal of Data Mining & Knowledge Management Process Pub Date : 2016-07-30 DOI: 10.5121/IJDKP.2016.6402
Ling Hu, Q. Ni
{"title":"Big Data-Driven Fast Reducing the Visual Block Artifacts of DCT Compressed Images for Urban Surveillance Systems","authors":"Ling Hu, Q. Ni","doi":"10.5121/IJDKP.2016.6402","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6402","url":null,"abstract":"The Urban Surveillance Systems generate huge amount of video and image data and impose high pressure onto the recording disks. It is obvious that the research of video is a key point of big data research areas. Since videos are composed of images, the degree and efficiency of image compression are of great importance. Although the DCT based JPEG standard are widely used, it encounters insurmountable problems. For instance, image encoding deficiencies such as block artifacts have to be removed frequently. In this paper, we propose a new, simple but effective method to fast reduce the visual block artifacts of DCT compressed images for urban surveillance systems. The simulation results demonstrate that our proposed method achieves better quality than widely used filters while consuming much less computer CPU resources.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588688","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
Analysis of Tuition Growth Rates Based on Clustering and Regression Models 基于聚类和回归模型的学费增长率分析
International Journal of Data Mining & Knowledge Management Process Pub Date : 2016-07-30 DOI: 10.5121/IJDKP.2016.6401
Long Cheng, Chenyu You
{"title":"Analysis of Tuition Growth Rates Based on Clustering and Regression Models","authors":"Long Cheng, Chenyu You","doi":"10.5121/IJDKP.2016.6401","DOIUrl":"https://doi.org/10.5121/IJDKP.2016.6401","url":null,"abstract":"Tuition plays a significant role in determining whether a student could afford higher education, which is one of the major driving forces for country development and social prosperity. So it is necessary to fully understand what factors might affect the tuition and how they affect it. However, many existing studies on the tuition growth rate either lack sufficient real data and proper quantitative models to support their conclusions, or are limited to focus on only a few factors that might affect the tuition growth rate, failing to make a comprehensive analysis. In this paper, we explore a wide variety of factors that might affect the tuition growth rate by use of large amounts of authentic data and different quantitative methods such as clustering and regression models.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131212550","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: 3
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