Classification Problem in Data Mining Using Decision Trees

The aim of this paper is to present the classification problem in data mining using decision trees. Simply stated, data mining refers to extracting or “mining” knowledge from large amounts of data. Data mining known by different names as knowledge mining, knowledge extraction, data/pattern analysis, data archaeology, data dredging, knowledge discovery in databases (KDD). Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Classification is an important problem in data mining. Given a database D= {t1,t2,.…, tn}and a set of classes C= {Cl,..…, Cm}, the Classification Problem is to define a mapping f: D → C where each it is assigned to one class. It means that given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. Actually classifier divides the database into equivalence classes that is each class contains same type of records.