An algorithm (model, method) is called a classification algorithm if it uses the data and its classification to build a set of patterns: discriminant and /or characteristic rules or other pattern descriptions.

Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. Classification—A Two-Step Process

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4

Data mining offers promising ways to uncover hidden patterns within large amounts of data. These hidden patterns can potentially be used to predict future behaviour.

Aug 7, 2018 · In this paper, we applied a complete text mining process and Naïve Bayes machine learning classification algorithm to two different data sets (tweets_Num1 and tweets_Num2) taken …

We illustrate the basic concepts of classification in this chapter with the following two examples. Example 3.1. [Vertebrate Classification] Table 3.2 shows a sample data set for classifying …

In this review article, we discuss a number of diferent classification algorithms used in data mining for unique applications. There are various techniques to analyse the data for continuous and discrete …

Finding the minimal subsets (reducts) of attributes for feature reduction is NP-hard but a discernibility matrix (which stores the differences between attribute values for each pair of data tuples) is used to …

The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data. Classification: Some of the …

Recent datamining research has built on such work, developing scalable classification and prediction techniques capable of handling large amounts of disk-resident data.