Download Data Mining and Knowledge Discovery Handbook (Springer by Oded Maimon, Lior Rokach PDF

By Oded Maimon, Lior Rokach

This booklet organizes key innovations, theories, criteria, methodologies, developments, demanding situations and functions of knowledge mining and information discovery in databases. It first surveys, then presents finished but concise algorithmic descriptions of equipment, together with vintage equipment plus the extensions and novel equipment constructed lately. It additionally provides in-depth descriptions of information mining functions in numerous interdisciplinary industries.

Show description

Read or Download Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences) PDF

Best data mining books

Fuzzy logic, identification, and predictive control

The complexity and sensitivity of recent business methods and structures more and more require adaptable complicated regulate protocols. those controllers must be in a position to take care of situations difficult ôjudgementö instead of uncomplicated ôyes/noö, ôon/offö responses, conditions the place an vague linguistic description is frequently extra suitable than a cut-and-dried numerical one.

Machine Learning and Cybernetics: 13th International Conference, Lanzhou, China, July 13-16, 2014. Proceedings

This publication constitutes the refereed lawsuits of the thirteenth foreign convention on laptop studying and Cybernetics, Lanzhou, China, in July 2014. The forty five revised complete papers provided have been rigorously reviewed and chosen from 421 submissions. The papers are prepared in topical sections on class and semi-supervised studying; clustering and kernel; software to acceptance; sampling and massive facts; software to detection; choice tree studying; studying and version; similarity and determination making; studying with uncertainty; more desirable studying algorithms and functions.

Intelligent Techniques for Data Science

This textbook presents readers with the instruments, thoughts and circumstances required to excel with sleek man made intelligence tools. those embody the family members of neural networks, fuzzy structures and evolutionary computing as well as different fields inside of computer studying, and may assist in deciding upon, visualizing, classifying and studying information to help company judgements.

Data Mining with R: Learning with Case Studies, Second Edition

Facts Mining with R: studying with Case reviews, moment variation makes use of sensible examples to demonstrate the ability of R and information mining. offering an intensive replace to the best-selling first variation, this new version is split into elements. the 1st half will function introductory fabric, together with a brand new bankruptcy that gives an creation to facts mining, to counterpoint the already latest creation to R.

Additional resources for Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

Sample text

Future research directions include the investigation and integration of various methods to address error detection. Combination of knowledge-based techniques with more general approaches should be pursued. In addition, a better integration of data cleansing in the data quality processes and frameworks should be achieved. The ultimate goal of data cleansing research is to devise a set of general operators and theory (much like relational algebra) that can be combined in well-formed statements to address data cleansing problems.

Archibald, B. , & Liu, X. Learning Approaches for Detecting and Tracking News Events, IEEE Intelligent Systems 1999; 14(4). , & Zhang, A. FindOut: Finding Outliers in Very Large Datasets, Knowledge and Information Systems 2002; 4(4):387-412. , Yuan, S. , & Ling, T. W. A new efficient data cleansing method. Proceedings of 13th International Conference on Database and Expert Systems Applications; 2002 September 02-06; 484-493. 3 Handling Missing Attribute Values Jerzy W. Grzymala-Busse1 and Witold J.

Thus |R’| is the number of records that the rule holds for and the confidence, c, of the rule is the percentage of records that hold for the rule c = |R’|/|R|. The process to identify potential errors in data sets using ordinal association rules is composed of the following steps: 1. Find ordinal rules with a minimum confidence c. , 1993). 2. Identify data items that broke the rules and can be considered outliers (potential errors). Here, the manner in which support of a rule is important differs from typical datamining problem.

Download PDF sample

Rated 4.49 of 5 – based on 15 votes