International Journal of Data Mining & Knowledge Management Process最新文献_第9页

Boundness of a Neural Network Weights Using the Notion of a Limit of a Sequence 用序列极限的概念研究神经网络权值的有界性

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-05-30 DOI: 10.5121/ijdkp.2014.4301

Hazem Migdady

引用次数: 3

Evaluation of Rule Extraction Algorithms 规则提取算法的评价

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-05-30 DOI: 10.5121/IJDKP.2014.4302

T. Gopikrishna

引用次数: 6

Mining High Utility Itemsets in Data Streams Based on the Weighted Sliding Window Model 基于加权滑动窗口模型的数据流高实用项集挖掘

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-03-31 DOI: 10.5121/IJDKP.2014.4202

P. S. Tsai

引用次数: 1

Accurate Time Series Classification Using Shapelets 使用Shapelets精确的时间序列分类

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-03-31 DOI: 10.5121/IJDKP.2014.4204

M. Arathi, A. Govardhan

引用次数: 3

Prediction of Quality Features in Iberian Ham by Applying Data Mining on Data From MRI and Computer Vision Techniques 应用MRI数据挖掘和计算机视觉技术预测伊比利亚火腿的品质特征

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-03-31 DOI: 10.5121/IJDKP.2014.4201

D. Caballero, A. Caro, T. Pérez-Palacios, P. G. Rodríguez, Ramón Palacios

引用次数: 2

Enhancing the Labelling Technique of Suffix Tree Clustering Algorithm 后缀树聚类算法标记技术的改进

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-01-31 DOI: 10.5121/IJDKP.2014.4104

R. Mahalakshmi, L. Praba

引用次数: 2

Recommending Tags for New Resources in Social Bookmarking Systems 社会化书签系统中为新资源推荐标签

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-01-31 DOI: 10.5121/IJDKP.2014.4102

Shweta Yagnik, Priyank Thakkar, K. Kotecha

{"title":"Recommending Tags for New Resources in Social Bookmarking Systems","authors":"Shweta Yagnik, Priyank Thakkar, K. Kotecha","doi":"10.5121/IJDKP.2014.4102","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4102","url":null,"abstract":"Social bookmarking system is a web-based resource sharing system that allows users to upload, share and organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of bookmarking from an individual activity limited to desktop to a collective activity on the web. It also facilitates user to annotate his resource with free form tags that leads to large communities of users to collaboratively create accessible repositories of web resources. Tagging process has its own challenges like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to user while annotating resource. In this paper, we propose a framework that does not depend on tagging history of the resource or user and thereby capable of suggesting tags to the resources which are being submitted to the system first time. We model tag recommendation task as multi-label text classification problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF) representation of the resources and fit appropriate distribution to the data based on the representation used. Impact of feature selection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed framework is evaluated through precision, recall and f-measure metrics.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115999228","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

Building a Collective-Experience Engine for Experience-Transfer Amongst Web Users 构建网络用户体验传递的集体体验引擎

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-01-31 DOI: 10.5121/IJDKP.2014.4101

J. K. Hall, Y. Kiyoki

{"title":"Building a Collective-Experience Engine for Experience-Transfer Amongst Web Users","authors":"J. K. Hall, Y. Kiyoki","doi":"10.5121/IJDKP.2014.4101","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4101","url":null,"abstract":"This paper describes the Collective Experience Engine (CEE), a system for indexing ExperientialKnowledge about Web knowledge-sources (websites), and performing relative-experience calculations between participants of the CEE. The CEE provides an in-browser interface to query the collective experience of others participating in the CEE. This interface accepts a list of URLs, to which the CEE adds additional information based on the Queryee's previously indexed Experiential-Knowledge. The core of the CEE is its Experiential-Context Conversation (ECConversation) functionality, whereby an collection of a person’s Web Experiential-Knowledge can be utilized to allow a real-world conversation-like exchange of information to take place, including adjusting information-flow based on the Queryee's experiential background and knowledge, and providing additional experientially-related knowledge integrated into the answer from multiple selected 'experience donors'. A relative-experience calculation ensures that information is retrieved only from relative-experts, to ensure sufficient additional information exists, but that such information isn't too advanced for the Queryee to process. This paper gives an overview of the CEE, and the underlying algorithms and data structures, and describes a system architecture and implementation details.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128532009","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

Dormancy Prediction Model in a Prepaid Predominant Mobile Market : A Customer Value Management Approach 预付费主导移动市场的休眠预测模型:一种客户价值管理方法

International Journal of Data Mining & Knowledge Management Process Pub Date : 2014-01-31 DOI: 10.5121/IJDKP.2014.4103

Adeolu O. Dairo, T. Akinwumi

{"title":"Dormancy Prediction Model in a Prepaid Predominant Mobile Market : A Customer Value Management Approach","authors":"Adeolu O. Dairo, T. Akinwumi","doi":"10.5121/IJDKP.2014.4103","DOIUrl":"https://doi.org/10.5121/IJDKP.2014.4103","url":null,"abstract":"Previous studies have predicted customer churn in the mobile indutry especially the postpaid customer segment of the market. However, only few studies have been published on the prepaid segment that could be used and operationalised within the marketing team that are responsible for the management of incident of prepaid churn. This is the first identifiable literature where customer dormancy is predicted along the customer value segmentation. In this article, we use a popular data mining technique to predict when a customer will go dormant or stop performing revenue generating events in a prepaid predominant market. Our study is unique as we considered ~1,451 attributes derived from CDR and SIM registration database (previous studies only considered maximum of ~1,381 potential variables). We built 3 different models for Very High, High and Low value segments. We applied our models on the prepaid base and the output was later compared with the actual dormant customers. Very High segment has the highest accuracy and lift while Low segment has the least at the same threshold. We show that once the problem of prepaid churn is well defined, it can be predicted. We recommend a value segmentation dormancy prediction with decision tree for prepaid segment with a certain threshold. Our study shows that this approach can be easily adopted and operationalised by the campaign management team responsible for the management of prepaid churn in a mobile industry.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125886098","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

Enhancement Techniques for Data Warehouse Staging Area 数据仓库暂存区的增强技术

International Journal of Data Mining & Knowledge Management Process Pub Date : 2013-11-30 DOI: 10.5121/IJDKP.2013.3601

Mahmoud El-Wessimy, Hoda M. O. Mokhtar, O. Hegazy

引用次数: 1