Data mining
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This document discusses data mining techniques and algorithms. It defines data mining as the process of analyzing data to find hidden patterns using statistical methods. It then covers various statistical models used in data mining like decision trees, clustering, Naive Bayes, and neural networks. It lists common business uses of data mining such as recommendation generation, anomaly detection, churn analysis, and targeted advertising. Finally, it provides more details on common data mining approaches like classification, clustering, regression, forecasting, sequence analysis, and deviation analysis.
Data mining
- 1. Data Mining by Shaoli Lu
- 2. What is data mining? • Data mining is the process of analyzing data to find hidden patterns using automatic statistical methodologies/algorithms/models • Use data as the “brain” • Predictive analytics is a subset of data mining • Ad hoc queries and OLAP are not suited to the task
- 3. Statistical Models • Decision trees • Clustering • Naïve Bayes • Neural network • Logistic regression • Time series • Associations
- 4. Business Cases • Recommendation generation • Anomaly detection • Churn analysis • Risk management • Customer segmentation • Targeted ads • Forecasting • Data exploration
- 5. Common Approaches • Classification • Clustering • Association • Regression • Forecasting • Sequence Analysis • Deviation Analysis
- 6. Classification • Classification is the most common data mining task. Business problems such as churn analysis, risk management, and targeted advertising usually involve classification • Supervised Machine Learning • Classification is the act of assigning a category to each case • Each case contains a set of attributes, one of which is the class attribute • The task requires finding a model that describes the class attribute as a function of input attributes • Typical classification algorithms include decision trees, neural network, and Naïve Bayes
- 7. Clustering • Clustering is also called segmentation. It is used to identify natural groupings of cases based on a set of attributes • Cases within the same group have more or less similar attribute values • Clustering is an unsupervised machine learning. There is no single attribute used to guide the training process, so all input attributes are treated equally
- 8. Association • Association is also called market basket analysis • Common usage of association is to identify common sets of items and rules for the purpose of cross-selling • The association task has two goals: to find those items that appear together frequently, and from that, to determine rules about the association
- 9. Regression • The regression task is similar to classification, except that instead of looking for patterns that describe a class, the goal is to find patterns to determine a numerical value • The most popular techniques used for regression are linear regression and logistic regression. SQL Server supports regression trees (part of the Microsoft Decision Trees algorithm) and neural networks • Support categorical inputs as well as numerical inputs
- 10. Forecasting • As input, it takes sequences of numbers indicating a series of values through time, and then it imputes future values of those series using a variety of machine-learning and statistical techniques that deal with seasonality, trending, and noisiness of data
- 11. Sequence Analysis • Sequence analysis is used to find patterns in a series of events called a sequence • Both sequence and time-series data are similar in that they contain adjacent observations that are order-dependent. The difference is that where a time series contains numerical data, a sequence series contains discrete states
- 12. Deviation Analysis • Deviation analysis is used to find rare cases that behave very differently from the norm • Widely used, fraud protection • There is no standard technique for deviation analysis, usually apply decision trees, clustering, or neural network algorithms for this task
- 13. Demo • Demo #1: Data Mining By Example – Building Predictive Model Using Microsoft Decision Trees