The Examination of Analyzing Data by Algorithm Performance

An algorithm is a specified set of rules/instructions that the computer will follow to solve a particular problem. In other words, we need to tell the computer how to process the data, so we can make sense of it. Data analysis has many facets, ranging from statistics to engineering. In this paper basic models and algorithms for data analysis are discussed [Songyi Xiao, Wenjun Wang, Hui Wang,2019]. Novel uses of cluster analysis, precedence analysis, and data mining methods are emphasized. The software for the cluster analysis algorithm and the triangularization is presented. The efficiency or complexity of the algorithm is nothing but the number of steps executed by the algorithm to achieve the results. In theoretical analysis of algorithms, it is common to estimate their complexity in asymptotic sense, i.e., to estimate the complexity function for reasonably large length of input. It's also easier to predict bounds for the algorithm than it is to predict an exact speed. Asymptotic notation is a shorthand way to write down and talk about 'fastest possible' and 'slowest possible' running times for an algorithm, using high and low bounds on speed. Big O notation, omega notation and theta notation are used to this end [Dr.N.Sairam & Dr.R.Seethalakshmi , 2010].