标题：Real Time Data Mining-based Intrusion Detection
作者：Wenke Lee, Salvatore J. Stolfo, and so on
时间：2001年
语言：英语 English
摘要：
"In this paper, we present an overview of our research in real time data mining-based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions; we use artificial anomalies along with normal and/or intrusion data to produce more effective misue and anomaly detection models. To improve efficiency, the computational costs of features are analyzed and a multiple-model cost-based approach is used to produce detection models with low cost and high accuracy. We also present a distributed architecture for evaluating cost-sensitive models in real-time. To improve usability, adaptive learing algorithms are used to facilitate model construction and incremental updates; unspuervised anomaly detection algorithms are used to reduce the reliance on labeled data. We also present an architecture consisting of sensors, detectors, a data warehouse, and model generation components. This architecture facilitates the sharing and storage of audit data and the distribution of new or updated models. This architecture also improves the efficiency and scalability of the IDS."
标题：Scientific Research Impact and Data Mining Applications in Hydrogeology
作者：Yao-chuen Fang
时间：2004年
语言：英语 English
摘要：
"This disseration focuses on the use of citation data to evaluate the impactfulness of research in hydrogeology. This study not only explores research impact, but also applies one of the most useful information technologies: data mining techniques on textutal data and a practical hydrogeological problem."
标题：Scaling Data Mining Algorithms to Large and Distributed Datasets
作者：Shashikumar G. Totad, Geeta R.B., Chennupati R Prasanna, N Krishna Santhosh, PVGD Prasad Reddy.
时间：2010年
语言：英语 English
摘要：
"In the contemporary world of global economy real-life data is distributed and evolving consistently. For the purpose of data mining, the large set of evolving and distributed data can be handled efficiently by Parallel Data mining and Distributed Data Mining, Incremental Data mining. In this paper, we discuss about the issues and the present research work that is being carried out on parallel and distributed data mining. Adaptability of some core data mining algorithms such as decision trees, discovery of frequent patterns, clustering, etc., for parallel processing and contemporary research work related to parallel processing of the algorithms is also discussed. We have identified two approaches for carrying out distributed data mining and tried to bring out the advantages of using mobile agents in client server-based approaches, in terms of bandwidth usage and network latency."
标题：Medical Data Mining on the Internet: Research on a Cancer Information System
作者：Andrea L. Houston, Hsinchun chen, Susan M. Hubbard, and so on
时间：1999年
语言：英语 English
摘要：
"This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Insititute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit dat mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their exisiting vast cancer information collections."
标题：Challenging Research Issues in Data Mining, Databases and Information Retrieval
作者：Aparna S. Varde
时间：2009年
语言：英语 English
摘要：
"Data mining research along with related fields such as databases and information retrieval poses challenging problems, especially for doctoral students. The research spreads over a variety of topics such as text mining, semantic web, multilingual information analysis, heterogeneous data management, database learning, digital libraries and more. Much of this research cuts across multiple fields and presents interesting issues for discussion at conferences with a confluence of several tracks. The ACM Conference on Information and Knoweldge Management provides an excellent environment for presenting such research problems spanning the three tracks of database systems, information retrieval and knowledge management. This article provides an overview of the disseration problems presented ata a Ph.D. workshop in the ACM Conference on Information and Knowledge Management. The goal of such workshops is to allow students to showcase their creative ideas at an early stage. This enables experts to critique their work and also gives the students an opportunity to exchange their thoughts with each other, besides providing excellent networking opportunities with industry and academia. This article provides an overview of the papers presented at the Ph.D workshop. It serves as a motivation for researchers to delve deeper into the innovative dissertation problems summarized here and the related work in these ares."
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.
扫码加好友，拉您进群
全部版块
我的主页
收藏
