K-Medoid Clustering Shows Negative Impact In Missing Data Imputation

Abstract

Missing Data Imputation imputes the missing values from the known values. Rather than imputing from the whole dataset, imputation techniques are applied in the clusters generated by using clustering algorithm. In this paper, K-Medoid clustering is used. But when compared the results in terms of accuracy, it seems that K-Medoid clusters are not suited for Missing Data Imputation.

Authors and Affiliations

R Malarvizhi, Dr. Antony Selvadoss Thanamani

Keywords

Related Articles

Self-assured Data Arrangement within Information Exhaustive Wireless Sensor Correlation

In the corpulent number of outgrowing viable environment each and everything depends on the other sources to transmit the data securely and maintain the data as well in the familiar medium. Transferable nodes in mili...

Biometric Security Techniques For IRIS Recognition System

Iris recognition is an automated method of biometric identification that uses mathematical patternrecognition techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique...

A New Filtering Technique for denoising Speckle Noise from Medical Images Based on Adaptive and Anisotropic Diffusion Filter

This is a preliminary study and the objective of this study has been to compare the performance of some of the primitive and fundamentally different post acquisition image enhancement algorithms as applied to differen...

A Novel approach on ɸ - Disposition Pulse Width Modulation for Modular Multilevel Inverter

In this project implemented a novel approach in the modulation technique based on selective virtual loop mapping, to attain dynamic capacitor voltage balance without any compensated signal.An improvised proposed metho...

Estimating the qos parameters and enhancing performance by implementing cluster head in MANET

In the MANET each node communicates with the other node temporarily and stops all the communication when all the data transfers done. Clustering (used in MANET) provides an effective way to allocate wireless channels...

Download PDF file
  • EP ID EP27534
  • DOI -
  • Views 407
  • Downloads 7

How To Cite

R Malarvizhi, Dr. Antony Selvadoss Thanamani (2013). K-Medoid Clustering Shows Negative Impact In Missing Data Imputation. International Journal of Research in Computer and Communication Technology, 2(1), -. https://europub.co.uk/articles/-A-27534