TITLE

Data mining

PUB. DATE
October 2000
SOURCE
Nature Biotechnology;Oct2000 Supplement 1, Vol. 18, p35
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The article presents a reprint of the article "Data mining" which appeared in the 2000 issue, volume 18 of "Nature Biotechnology." It focuses on the contribution of data mining in the healthcare industry by providing companies a vast array of software products and services to clients that generate large quantity of data. A list of selected companies offering specific data-mining products and services targeted to the biopharmaceutical industry is offered.
ACCESSION #
23291367

 

Related Articles

  • Design and Implementation of School Hospital Information Analysis and Mining System. Wang xuesong; Guo Qiang; Li Shanshan; Cao Rongfei // Applied Mechanics & Materials;2014, Issue 513-517, p498 

    Hospital information analysis is a very important way to enhance the medical service. In this paper, the hospital information analysis system of university is implemented for revealing hidden information. First, the processing model of medical information is studied. Then, the multi-layer...

  • Exploiting Temporal Relations in Mining Hepatitis Data. Tu-Bao Ho; Canh-Hao Nguyen; Kawasaki, Saori; Si-Quang Le; Takabayashi, Katsuhiko // New Generation Computing;2007, Vol. 25 Issue 3, p247 

    Various data mining methods have been developed last few years for hepatitis study using a large temporal and relational database given to the research community. In this work we introduce a novel temporal abstraction method to this study by detecting and exploiting temporal patterns and...

  • Bio Tagger-GM: A Gene/Protein Name Recognition System. TORII, MANABU; ZHANGZHI HU; WU, CATHY H.; HONGFANG LIU // Journal of the American Medical Informatics Association;Mar/Apr2009, Vol. 16 Issue 2, p247 

    Objectives: Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name...

  • The Research of Distributed Data Mining Knowledge Discovery Based on Extension Sets.  // International Journal of Computer Applications;Oct2010, Vol. 8, p12 

    The article offers information on the importance of distributed data mining knowledge to researchers in Pradesh, India. It says that this method is vital to them because it helps them discover and generate new knowledge from large databases. Moreover, it furnishes methods in obtaining...

  • Data Mining is Dead--Long Live Predictive Analytics. Agosta, Lou // DM Review;Jan2004, Vol. 14 Issue 1, p37 

    Compares the data mining and predictive analytics techniques in the U.S. Reasons for the failure of data mining; Functions of the Data Mining software of Oracle; Formulation and validation of hypothesis in data mining.

  • System for Biomedical Information Search in the Web. Cabrera, R. Guzmán; Sosa, J. A. Gordillo; Parada, A. González; Manzano, O. G. Ibarra; Cisneros, M. Torres // International Proceedings of Chemical, Biological & Environmenta;2012, Vol. 34, p36 

    Due to the dramatic increase on available information on the Web, the users are in continuous demand of the appearance of new tools in order to find, filtrate and analyze the huge amount of data returned via search engines. In this paper we present a new tool for Web searching which starts from...

  • A review on particle swarm optimization algorithms and their applications to data clustering. Rana, Sandeep; Jasola, Sanjay; Kumar, Rajesh // Artificial Intelligence Review;Mar2011, Vol. 35 Issue 3, p211 

    Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical...

  • An Efficient Density-based Approach for Data Mining Tasks. Domeniconi, Carlotta; Gunopulos, Dimitrios // Knowledge & Information Systems;Nov2004, Vol. 6 Issue 6, p750 

    We propose a locally adaptive technique to address the problem of setting the bandwidth parameters for kernel density estimation. Our technique is efficient and can be performed in only two dataset passes. We also show how to apply our technique to efficiently solve range query approximation,...

  • Characterizing and Mining the Citation Graph of the Computer Science Literature. An, Yuan; Janssen, Jeannette; Milios, Evangelos E. // Knowledge & Information Systems;Nov2004, Vol. 6 Issue 6, p664 

    Citation graphs representing a body of scientific literature convey measures of scholarly activity and productivity. In this work we present a study of the structure of the citation graph of the computer science literature. Using a web robot we built several topic-specific citation graphs and...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics