• 7.1: Data Mining Overview

    • Introduction to Data Mining Book

      Data mining involves various algorithms and techniques for database searching, inferring data relationships, pattern recognition, and pattern classification. Pattern recognition is the process of comparing a sample observation against a fixed set of patterns (like those stored in a database) to search for an optimal match. Face recognition, voice recognition, character recognition, fingerprint recognition, and text string matching are all examples of pattern searching and pattern recognition.

      Going one step further, given a set of prescribed pattern classes, pattern classification is the process of associating a sample observation with one of the pattern classes. For example, consider a database containing two possible classes of face images: happy faces and sad faces. Pattern classification involves processing an input face image of unknown classification to optimally classify it as either happy or sad. As you will soon see, the optimal pattern match or pattern classification is often defined using probabilistic and statistical measures such as distances, deviations, and confidence intervals.

    • Introduction to Machine Learning Page

      Machine learning is the aspect of data mining that applies algorithms for learning and inferring relationships within empirical data sets. Since machine learning often involves pattern searching and classification, it is a broad subject that encompasses several approaches for constructing data learning and inference models.

    • Bayes' Theorem Page

      Pattern search and classification problems often involve the application of observing data subject to some set of conditions. Study the relationship between conditional probability and Bayes' Theorem as it is the foundational material for data mining.

    • Bayes' Theorem and Conditional Probability Page

      Here are more examples of applying Bayes' Theorem and conditional probability to data mining.

    • Methods for Pattern Classification Book

      At the heart of all pattern search or classification problems (either explicitly or implicitly) lies Bayes' Decision Theory. Bayes' decision simply says, given an input observation of unknown classification, make the decision that will minimize the probability of a classification error. For example, in this unit, you will be introduced to the k-nearest neighbor algorithm. It can be demonstrated that this algorithm can make Bayes' decision. Read this chapter to familiarize yourself with Bayes' decision.