Computational Intelligence in Data Mining - Volume 3: by Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal,

By Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal, Durga Prasad Mohapatra

The contributed quantity goals to explicate and handle the problems and demanding situations for the seamless integration of 2 middle disciplines of machine technology, i.e., computational intelligence and information mining. information Mining goals on the computerized discovery of underlying non-trivial wisdom from datasets via utilising clever research concepts. The curiosity during this examine region has skilled a substantial progress within the final years as a result of key components: (a) wisdom hidden in enterprises’ databases will be exploited to enhance strategic and managerial decision-making; (b) the massive quantity of knowledge controlled by means of corporations makes it most unlikely to hold out a guide research. The ebook addresses various tools and strategies of integration for reinforcing the general target of information mining. The booklet is helping to disseminate the data approximately a few cutting edge, energetic study instructions within the box of knowledge mining, laptop and computational intelligence, in addition to a few present concerns and purposes of similar topics.

Show description

Read or Download Computational Intelligence in Data Mining - Volume 3: Proceedings of the International Conference on CIDM, 20-21 December 2014 PDF

Similar data mining books

Mining Imperfect Data: Dealing with Contamination and Incomplete Records

Info mining is worried with the research of databases big enough that a number of anomalies, together with outliers, incomplete facts files, and extra sophisticated phenomena reminiscent of misalignment blunders, are almost absolute to be current. Mining Imperfect information: facing illness and Incomplete files describes intimately a couple of those difficulties, in addition to their assets, their effects, their detection, and their remedy.

Unsupervised Information Extraction by Text Segmentation

A brand new unsupervised method of the matter of knowledge Extraction by means of textual content Segmentation (IETS) is proposed, carried out and evaluated herein. The authors’ process is determined by details to be had on pre-existing information to profit the way to affiliate segments within the enter string with attributes of a given area hoping on a really powerful set of content-based good points.

Computational Science and Its Applications – ICCSA 2014: 14th International Conference, Guimarães, Portugal, June 30 – July 3, 2014, Proceedings, Part VI

The six-volume set LNCS 8579-8584 constitutes the refereed lawsuits of the 14th foreign convention on Computational technological know-how and Its purposes, ICCSA 2014, held in Guimarães, Portugal, in June/July 2014. The 347 revised papers provided in 30 workshops and a unique song have been rigorously reviewed and chosen from 1167.

Handbook of Educational Data Mining

Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy and Ryan S. J. d. Baker, «Handbook of academic information Mining» . guide of academic info Mining (EDM) presents an intensive evaluation of the present country of information during this quarter. the 1st a part of the publication contains 9 surveys and tutorials at the vital info mining options which have been utilized in schooling.

Extra resources for Computational Intelligence in Data Mining - Volume 3: Proceedings of the International Conference on CIDM, 20-21 December 2014

Example text

When the interactive effects between projects are not considered, the optimization problem in terms of Eq. (1) for project selection can be formulated as Max E ¼ 20:793x1 þ 3:897x2 þ 8:378x3 þ 1:214x4 þ 344:213x5 þ 99:798x6 þ 107:477x7 ð6Þ s:t: x1 þ x2 þ x3 þ x4 þ x5 þ x6 þ x7 ¼ 2 xn ¼ f0; 1g; n ¼ 1; 2; . 17 Table 2 Data which was obtained by using the Eq. 22 Table 4 Comparison of objective value and computational time for GA-based method and CPLEX optimizer for seven variables Equation no. 2201 9 13 11 15 If interactive effects between projects are considered, then the problem will become complex and from Eq.

Fitness function: Each individual is evaluated by using the fitness function in GA. The GA is designed to maximize the fitness value. Generally the fitness function defines a score which gives each chromosome the probability to be chosen for reproduction or to survive. 4. Selection: Selection operator is used to create the population with higher fitness. Here, roulette wheel approach is used to select chromosomes from the current population with higher fitness. 5. Crossover: The main goal of crossover operator is to generate different offspring chromosomes to obtain a more optimal solution than their parents.

Org the official site for NLTK 12. shtmt Parser and POS Tagger 13. : Construction of image ontology using low level for image retrivel. In: Proceedings of the International Conference on Computer Communication and Informatics, (ICCCI 2012), pp. 129–134 (2012) 14. : Effective visualization of conceptual class diagrams. In: Proceedings of International Conference on Recent Advances in Computing and Software Systems. pp. 1–6 (2012). 6212688 15. : Object-Oriented Modeling and Design. Pearson Education, Ghaziabad (1991) 16.

Download PDF sample

Rated 4.71 of 5 – based on 31 votes