Computational Mining Algorithms for Future Technologies

Abstract

Extensive analysis and research done on data mining has led to successful results such as algorithms, techniques, equipment, and various research approaches used for catering vast number of data that is purposed for certain uses which include problem solving techniques. Many application domains use data mining techniques, which are useful in creating foreseen analysis, housing, business intelligence, creating a good decision system, and used in bio-informatics. The primary aim intended for data mining is efficiently solving extensive data, extracting actionable series and acquiring perceptive understanding. Data mining is a sole part of knowledge discovery within database (KDD) activities. Triumph and enhanced decision implementation process is relied based on how fast the data can be identified and analyzed. Such intuitions can be highly be used in preparing effective actions that can be utilized within operational processes, and foreseeing a better future. The focus of the paper is outlaying an overview of certain algorithms that are suitable for solving extensive data sets. The prospected algorithms explain certain structures and techniques that have been enacted in solving big data. Additionally, the paper evaluates the common strengths and restrictions for the mentioned algorithms. Hence, the paper may guide or is an opener used by data mining researchers where algorithms can be selected and used in handling challenges that will be analyzed.