Provided by: Science and Development Network (SciDev.Net)
Topic: Big Data
Date Added: Jun 2012
Data mining is the process of extracting useful and yet unknown information such as patterns or associations hidden in stored data. Among various existing techniques applied to search for interesting patterns, decision tree is one of the most popular tools used for data mining. Most data mining techniques are data-driven, however, data repositories of interest in data mining applications can be very large and noisy. Noise is a random error in data. Noise in a data set can happen in different forms: misclassification or wrong labeled instances, erroneous or distorted attribute values, contradictory or duplicate instances having different labels. All kinds of noise can more or less affect the learning performance.