Hypothyroid Analysis with Emcluster using Frequent Pattern Mining Techniques

Abstract

Data mining is a process that involves
identifying sequential patterns within large datasets. In
the data analysis process, cluster classification analysis
and machine intelligence techniques are commonly
employed. Data mining plays a crucial role in enhancing
the analysis of medical bioinformatics data.
Classification algorithms are used to predict outcomes,
while association analysis helps in identifying rules
associated with items that frequently co-occur. The
Weka software is a powerful tool that encompasses data
pre-processing tools, classification and regression
algorithms, clustering algorithms, and association rule
mining algorithms. It also includes attribute and subset
evaluation methods for feature selection. Weka supports
multiple platforms and is written in Java, making it
widely accessible and versatile. It allows users to define
filters for transforming data through processes such as
discretization, normalization, resampling, and attribute
selection. In the context of bioinformatics, gene
expression analysis can be performed using Weka to
predict the accuracy of frequent pattern mining
algorithms in diagnosing conditions such as
hypothyroidism. By leveraging the capabilities of Weka
and data mining techniques, researchers can gain
valuable insights and make informed decisions based on
complex biological data.