Data Mining Tasks

Data mining tasks are generally divided into

Predictive tasks

to predict the value of a particular attribute based on the values of other attributes.

Attribute to be predicted - target or dependent variables.

Attributes used for making the prediction - explanatory or independent variables

Descriptive tasks

to derive patterns (correlations, trends, clusters, trajectories, and anomalies) that summarize the underlying relationships in data.

It is exploratory in nature

It requires postprocessing techniques to validate and explain the results

Core data mining tasks

Predictive Modeling

Association Analysis

Cluster Analysis

Anomaly Detection

Predictive Modeling

task of building a model for the target variable as a function of the explanatory variables.

Two types of predictive tasks

Regression

used for continuous target variables

Classification

used for discrete/categorical target variables

Example:

Predicting disease of a patient

Association Analysis

used to discover patterns that describe strongly associated features in the data.

Example

Identifying products bought together

Cluster Analysis

used to identify groups of closely related observations

Example

Grouping articles to the related topics

Anomaly Detection

task of identifying observations whose characteristics are significantly different from the rest of the data.

A good anomaly detector must have a high detection rate and a low false alarm rate.

Example

Detecting credit card fraud

Gaussian Elimination - Row reduction Algorithm

Gaussian elimination is a method for solving matrix equations of the form, Ax=b. This method is also known as the row reduction algorithm. Back Substitution Solving the last equation for the variable and then work backward into the first equation to solve it. The fundamental idea is to add multiples of one equation to the others in order to eliminate a variable and to continue this process until only one variable is left. Pivot row The row that is used to perform elimination of a variable from other rows is called the pivot row. Example: Solving a linear equation The augmented matrix for the above equation shall be The equation shall be solved using back substitution. The eliminating the first variable (x1) in the first row (Pivot row) by carrying out the row operation. As the second row become zero, the row will be shifted to bottom by carrying out partial pivoting. Now, the second variable (x2) shall be eliminated by carrying out the row operation again. ...

Decision Tree - Gini Index

The Gini index is used in CART. The Gini index measures the impurity of the data set, where p i - probability that data in the data set, D belong to class, C i and pi = |C i,D |/|D| There are 2 v - 2 possible ways to form two partitions of the data set, D based on a binary split on a attribute. Each of the possible binary splits of the attribute is considered. The subset that gives the minimum Gini index is selected as the splitting subset for discrete valued attribute. The degree of Gini index varies between 0 and 1. The value 0 denotes that all elements belong to a certain class or if there exists only one class, and the value 1 denotes that the elements are randomly distributed across various classes. A Gini Index of 0.5 denotes equally distributed elements into some classes. The Gini index is biased toward multivalued attributes and has difficulty when the number of classes is large.

Data Cleaning Process

Data cleaning attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data. Data cleaning is performed as an iterative two-step process consisting of discrepancy detection and data transformation. The missing values of the attribute can be addressed by Ignoring the value filling the value manually Using global constant to fill the value Using a measure of central tendency (mean or median) of value Using attribute mean or median belonging to same class Using the most probable value Noise is a random error or variance in a measured variable. The noisy data can be smoothened using following techniques. Binning methods smooth a sorted data value by consulting the nearby values around it. smoothing by bin means - each value in a bin is replaced by the mean value of the bin. smoothing by bin medians - each bin value is replaced by the bin median smoothing by bin boundaries - the minimum and maximum values in a ...

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