An Improved Efficient FP-Growth Algorithm Using FP-TDA Algorithm

Abstract

Mining association rules is one of the most important tasks for uncovering hidden patterns in datasets to aid organizations in making efficient decisions based on customer behavior and preference. This association rule identifies other possible connections that may exist beyond customer preference and behavior. Some of the most prominent algorithms used in this regard are Apriori algorithms and FP-Growth algorithms. They help to obtain sufficient information based on huge datasets. However, association rule mining using existing algorithms has encountered several problems. They consume a huge memory space for data processing. They need data searching for finding out the frequency of each item. They also establish rules with low efficiency. In overcoming such problems engendered by data mining association rules, a better efficient Fp-TDA algorithm using advanced FP-Growth algorithms has recently been developed. This algorithm uses Matrix Tree instead of a tree concept used in existing algorithms. This largely cuts down data processing time. This concept largely cuts down frequently generated data. This concept largely addresses precision. This largely enhances data processing. This concept largely addresses efficiency. This largely addresses data quality. This largely addresses data time. This concept largely addresses memory space. The Fp-TDA largely addresses problems in association rule mining. This concept largely addresses data processing. This concept largely addresses data quality. This concept largely addresses data memory