NDLI: A classification method to classify high dimensional data

Content Provider	IEEE Xplore Digital Library
Author	Gupta, A. Chetty, N. Shukla, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Graphic Era Hill Univ., Dehradun, India (Gupta, A.) \|\| Sch. of Comput. Sci. & Eng., Galgotias Univ., Noida, India (Shukla, S.) \|\| Dept. of Comput. Sci. & Eng., Mangalore Inst. of Technol. & Eng., Mangalore, India (Chetty, N.)
Abstract	The rapid computerization and advancement in the technology has led to huge amount of data in the databases. Research has shown that the amount of data in the world doubles in every 20 months. However, this available data consists of large number of noise values and thus, cannot be directly used. The extraction of information from the vast pool of data has emerged a major challenge. Machine learning techniques have emerged as an effective tool to overcome this challenge. Several machine learning algorithms (like SVM, K-means etc.) are effectively applied in data mining. In this paper author have applied classification and clustering techniques on different datasets and have proposed a model for enhancing the performance of K-means data clustering method and Naïve Bayes data classification method. The efficiency of the proposed model is calculated based on general parameters like accuracy, precision, recall, F-measure and number of iterations.
Starting Page	1
Ending Page	6
File Size	371208
Page Count	6
File Format	PDF
e-ISBN	9781467393546
DOI	10.1109/CCCS.2015.7374132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-04
Publisher Place	Mauritius
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Preprocessing Redundancy K-Means Data Mining Classification algorithms Data mining Clustering Naïve Bayes Classification Databases Clustering algorithms Data models Mathematical model WEKA tool
Content Type	Text
Resource Type	Article

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