Estimation of Data Parameters Using Cluster Optimization

EasyChair Preprint 7293

Abstract

Machine learning is the kind of process to turns out to be an undividable fraction of any methodical work is mostly because of the simplicity of use and the simplicity of generation of data. The data generation task is pricey or tedious, the data is usually generated in parallel amid various research groups and they are communal by scientists. For this activity is the initial task, is frequently to cluster the data into several categories to set up which data from what source are related to each other. In this paper, optimization is usually is implemented in use for such a clustering activity is proposed. When the data is accomplished then they are entire jumbled jointly. One way to prepare an optimization difficulty is to primary decide on a number of clusters that the data may be separated to. After that, for each cluster in not many parameters are used variables for the optimization task. The parameters have to explain a similarity role for a cluster. The activity can be achieved through an optimization solve the prediction activity can be achieved using an optimization process. This process is used a Semi-Supervised learning approach and Data Mining like the K-Nearest Neighboring process and form the path route cluster. The prediction parameter of SRGM is approximated based upon these data clusters using Least Square Estimation.

Keyphrases: Cluster Optimization, Data Mining, K-Nearest Neighbour, least square method, semi-supervised learning

@booklet{EasyChair:7293,
  author    = {Dileep Kumar Kadali and M Chandra Naik and R.N.V. Jagan Mohan},
  title     = {Estimation of Data Parameters Using Cluster Optimization},
  howpublished = {EasyChair Preprint 7293},
  year      = {EasyChair, 2022}}