Machine learning algorithms for data categorization and analysis in communication

Author

Tan Xian

Author_Institution

Coll. of literature & Commun., Hubei Univ. for Nat., Enshi, China

fYear

2012

fDate

14-16 Aug. 2012

Firstpage

1

Lastpage

3

Abstract

Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base stations and also the noise levels in the busy hour can be predicted. 348 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari. Signal to noise ratio has been predicted at 55, based on CART results. About 53% instances provided inside the cluster and 47% provided outside the cluster. DBScan clustering provided maximum noise from srikakulam. MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.

Keywords

data analysis; learning (artificial intelligence); mobility management (mobile radio); pattern clustering; search problems; telecommunication computing; telecommunication traffic; CART results; DBScan clustering; J48 pruned tree; MOR; base stations; communication; data analysis; data categorization; east Godavari; genetic search; heavy traffic hours; machine learning algorithms; pattern recognition; traffic levels; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Data mining; Machine learning; Mobile communication; Prediction algorithms; Data mining; MOR; Traffic;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Security and Intelligence Control (ISIC), 2012 International Conference on

Conference_Location

Yunlin

Print_ISBN

978-1-4673-2587-5

Type

conf

DOI

10.1109/ISIC.2012.6449693

Filename

6449693