{"id":262805,"date":"2026-07-14T14:09:23","date_gmt":"2026-07-14T11:09:23","guid":{"rendered":"https:\/\/1kitap1.com\/en\/heuristic-and-optimization-for-knowledge-ruhul-sarker-1\/"},"modified":"2026-07-14T14:09:23","modified_gmt":"2026-07-14T11:09:23","slug":"heuristic-and-optimization-for-knowledge-ruhul-sarker-1","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/heuristic-and-optimization-for-knowledge-ruhul-sarker-1\/","title":{"rendered":"Heuristic And Optimization For Knowledge – Ruhul Sarker (1)"},"content":{"rendered":"
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Nevertheless, it is obvious that, all other things being equal, using the whole population will never be worse even if it is not significantly better. There are two major difficulties with this position. First, other things seldom are equal. In most practical situations, resources are limited and appropriate sampling strategies can lead to more effective use of limited resources. Second, the issue of sampling is usually inescapable. In data mining we are seldom given the whole of a population. Even very large data sets are usually samples of some larger population.<\/p>\n

An objection to sampling in principle is not a coherent position because data mining is almost always concerned with data sets that are themselves samples. A principled approach to data mining must therefore include a systematic consideration of the effect of sample size. Confidence Limits on Proportion Estimates The majority of machine learning procedures seek to discover associations between particular sets of attribute values. Most of them do this by counting, explicitly or implicitly, the occurrences of particular attribute value combinations in a training set and hence estimating the proportion of the population in which those combinations are found.<\/p>\n

These proportion estimates are often interpreted as prob- abilities. Consequently, the question of what effect sample size has on the accuracy of such proportion estimates is central to any examination of the effective and efficient mining of very large data sets. The mathematical development of sampling theory assumes that a random sample has been taken from a larger population. A range of random sampling procedures are considered in the literature (Kish, 1965) but only a few of these are relevant to this discussion. A simple random sampling (SRS) procedure is defined as one that is equally likely to choose any of the possible subsets of the specified number of items from the population.<\/p>\n

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Copyright \u00a9 2002 by Idea Group Publishing. All rights reserved. No part of this book may be reproduced in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Library of Congress Cataloging-in-Publication Data Heuristics and optimization for knowledge discovery \/ [edited by] Ruhul Sarker, Hussein Abbass, Charles Newton. p. cm. Includes bibliographical references and index.<\/p>\n

ISBN 1-930708-26-2 (cloth) 1. Heuristic programming. 2. Combinatorial optimization. I. Sarker, Ruhul. II. Abbass, Hussein. III. Newton, Charles, 1942- T57.84 .H48 2001 006.3–dc21 2001039720 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. NEW from Idea Group Publishing Excellent additions to your library! Receive the Idea Group Publishing catalog with descriptions of these books by calling, toll free 1\/800-345-4332 or visit the IGP Online Bookstore at: http:\/\/www.idea-group.com!<\/p>\n

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Rogers\/ 1-930708-28-9 \u2022 Heuristic and Optimization for Knowledge Discovery Ruhul Amin Sarker, Hussein Aly Abbass and Charles Newton\/ 1-930708-26-2 \u2022 Distributed Multimedia Databases: Techniques and Applications Timothy K.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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