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标题：Advanced Data Mining and Integration Research for Europe
作者：Malcolm Atkinson, Peter Brezany, and so on
时间：2008年
语言：英语 English
摘要：
"The rapidly growing wealth, complexity and diversity of data open many new opportunities in business, research, design, policy formulation and decision making. These opportunities will not be explored unless we advance the state of the art in integrating and analysing data. The European project Advanced Data Mining and Integration Research for Europe (ADMIRE) is pioneering architectures and models that will deliver a coherent, extensible and flexible framework to make the best use of a wide range of distributed data resources. The new architecture will improve the accessibility and exploitation of data for domain experts and provide a workbench for data mining and integration experts which will, in turn, improve their productivity and the rate of deployment of new methods and applications. The evisaged architecture will permit informatics and data engineering approaches by dividing responsibilities across three communities of experts and by supporting interfaces between their concerns. ADMIRE's approach partitions the architecture into three levels of discourse: the tools that support domain and data mining experts, a canonical model that supports communication about the processes supported by ADMIRE, and a data-aware distributed computing platform that delivers performance, efficiency and resilience. Usage scenarios in significantly different domains will illustrate how to use effectively the framework, tools and platform that ADMIRE will pioneer to demonstrate the effectiveness of its design. We identify curcial research questions and speculate on the impact of answering them effectively. A set of principles that will guide the work are reported and the partners is introduced."
是否有不完整的部分：完整的论文
Work Cited: http://www.admire-project.eu/docs/ADMIRE-WhitePaper.pdf
论文附件：
数据挖掘论文 11.pdf
(662.97 KB, 需要: 1 个论坛币)
第12篇
标题：Apply On-Line Analytical Processing (OLAP) With Data Mining For Clinical Decision Support
作者：Dr. Walid Qassim Qwaider
时间：2012年
语言：英语 English
摘要：
"Medicine is a new direction in his mission is to prevent, diagnose and medicate diseases using OLAP with data mining. Are analyzed clinical data on patient population and the wide range of performance management of health care, unfortunately, are not converted to useful information for effective decision-making. Built OLAP and data mining techniques in the field of health care, and an easy to use decision support platform, which supports the decision-making process of caregivers and clinical managers. This paper presents a model for clinical decision support system which combines the strengths of both OLAP and data mining. It provides a knowledge rich environment that cannot be achieved by using OLAP or data mining alone."
是否有不完整的部分：完整的论文
Work Cited: http://airccse.org/journal/ijmit/papers/4112ijmit03.pdf
论文附件：
数据挖掘论文 12.pdf
(770.29 KB, 需要: 1 个论坛币)
第13篇
标题：Stream Data Mining
作者：Hebah H.O. Nasereddin
时间：2009年
语言：英语 English
摘要：
"Data mining is a part of process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations between specific values of categorical variables in large data sets. There may be thousands or millions of records that have to be read and to extract the rules for, but the questions is what will happen if there is new data, or there is a need to modify or delete some or all the exisiting set of data during the process of data mining. In the past user would repeat the whole procedure, which is time-consuming in addition to its lack of efficiency. From this, the importance of dynamic data mining process appears and for this reason this problem is going to be the main topic of this paper. Therefore the purpose of this study is to find solution for dynamic data mining process that is able to take into considerations all updates (insert, update, and delete problems) into account."
是否有不完整的部分：完整的论文
Work Cited: http://www.dline.info/IJWA%20sample%20paper.pdf
论文附件：
数据挖掘论文 13.pdf
(406.87 KB, 需要: 1 个论坛币)
第14篇
标题：Performance Analysis of Data Mining Techniques for Placement Chance Prediction
作者：V. Ramesh, P. Parkavi, and P. Yasodha.
时间：2011年
语言：英语 English
摘要：
"Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to investigate the accuracy of data mining techniques in such an environment. The first step of the study is to gather student's data. We collected records of 300 Under Graduate students of computer science course, from a private Educational Institution. The second step is to clean the data and choose the relevant attributes. In the third step, NaiveBayesSimple, MultiLayerPerception, SMO, J48, REPTree algorithms were constructed and their performances were evaluated. The study revealed that the MultiLayerPerception is more accurate than the other algorithms. This work will help the institute to accurately predict the performance of the students."
是否有不完整的部分：完整的论文
Work Cited: http://www.ijser.org/researchpaper/Performance-Analysis-of-Data-Mining-Techniques-for-Placement-Chance-Prediction.pdf
论文附件：
数据挖掘论文 14.pdf
(436.98 KB, 需要: 1 个论坛币)
第15篇
标题：Mining Educational Data to Analyze Students' Performance
作者：Brijesh Kumar Baradwaj, Saurabh Pal
时间：2011年
语言：英语 English
摘要：
"The main objective of higher education insitutions is to provide quality to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrollment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here.
By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/conseling."
是否有不完整的部分：完整的论文
Work Cited: http://arxiv.org/ftp/arxiv/papers/1201/1201.3417.pdf
论文附件：
数据挖掘论文 15.pdf
(480.13 KB, 需要: 1 个论坛币)
声明：这几篇文章只供学术交流使用，不得用于其他用途。原文所有权利都由原作者保留。如果大家想要引用文章里的任何部分，请联系原作者。谢谢合作。
Announcement: All of these academic research papers only can be used for academic communication rather than any other purposes. These original papers were owned by their authors. If you want to use any part of these papers to write a research paper, please contact original authors. Thanks for your cooperation.
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