Classification Rules and Genetic Algorithm in Data Mining

1
Mr. Puneet Chadha
Mr. Puneet Chadha
1 Department of Computer Science, D.A.V. College, Sector-10, Affiliated to Panjab University, Chandigarh-160010

Send Message

To: Author

GJCST Volume 12 Issue C15

Article Fingerprint

ReserarchID

CSTSDE09ARH

Classification Rules and Genetic Algorithm in Data Mining Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Databases today are ranging in size into the Tera Bytes. It is an information extraction activity whose goal is to discover hidden facts contained in databases. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Major Data Mining Tasks and processes include

8 Cites in Articles

References

  1. Gregory Piatetsky (2007). Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from "university" to "business" and "analytics.
  2. Hans-Peter Kriegel,Karsten Borgwardt,Peer Kröger,Alexey Pryakhin,Matthias Schubert,Arthur Zimek (2007). Future trends in data mining.
  3. Qi Luo (2008). Advancing Knowledge Discovery and Data Mining" Knowledge Discovery and Data Mining.
  4. Gary Weiss,Bianca Zadrozny,Saar-Tsechansky (2008). Guest editorial: special issue on utility-based data mining.
  5. Ben Weber,Michael Mateas (2009). A data mining approach to strategy prediction.
  6. Sufal Das,Banani Saha (2009). Data Quality Mining using Genetic Algorithm.
  7. Atul Kamble (2010). Incremental Clustering in Data Mining using Genetic Algorithm.
  8. Murat Kantarcıoğlu,Bowei Xi,Chris Clifton (2011). Classifier evaluation and attribute selection against active adversaries.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Mr. Puneet Chadha. 2012. \u201cClassification Rules and Genetic Algorithm in Data Mining\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 12 (GJCST Volume 12 Issue C15): .

Download Citation

Issue Cover
GJCST Volume 12 Issue C15
Pg. 51- 54
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
Not Found
Version of record

v1.2

Issue date

December 11, 2012

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 10307
Total Downloads: 2631
2026 Trends
Research Identity (RIN)
Related Research

Published Article

Databases today are ranging in size into the Tera Bytes. It is an information extraction activity whose goal is to discover hidden facts contained in databases. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Major Data Mining Tasks and processes include

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Classification Rules and Genetic Algorithm in Data Mining

Mr. Puneet Chadha
Mr. Puneet Chadha Department of Computer Science, D.A.V. College, Sector-10, Affiliated to Panjab University, Chandigarh-160010

Research Journals