Question

1. List the steps of data mining processes and the corresponding major methods.

2. What are the common accuracy metrics for data-mining algorithms?

3. Search the available literature for additional metrics that measure algorithms for accuracy, suitability for a particular purpose, etc.

Answer 1

Steps In The Data Mining Process

The data mining process is divided into two parts i.e. Data Preprocessing and Data Mining. Data Preprocessing involves data cleaning, data integration, data reduction, and data transformation. The data mining part performs data mining, pattern evaluation and knowledge representation of data.

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Why do we preprocess the data?

There are many factors that determine the usefulness of data such as accuracy, completeness, consistency, timeliness. The data has to quality if it satisfies the intended purpose. Thus preprocessing is crucial in the data mining process. The major steps involved in data preprocessing are explained below.

#1) Data Cleaning

Data cleaning is the first step in data mining. It holds importance as dirty data if used directly in mining can cause confusion in procedures and produce inaccurate results.

Basically, this step involves the removal of noisy or incomplete data from the collection. Many methods that generally clean data by itself are available but they are not robust.

This step carries out the routine cleaning work by:

(i) Fill The Missing Data:

Missing data can be filled by methods such as:

• Ignoring the tuple.
• Filling the missing value manually.
• Use the measure of central tendency, median or
• Filling in the most probable value.

(ii) Remove The Noisy Data: Random error is called noisy data.

Methods to remove noise are :

Binning: Binning methods are applied by sorting values into buckets or bins. Smoothening is performed by consulting the neighboring values.

Binning is done by smoothing by bin i.e. each bin is replaced by the mean of the bin. Smoothing by a median, where each bin value is replaced by a bin median. Smoothing by bin boundaries i.e. The minimum and maximum values in the bin are bin boundaries and each bin value is replaced by the closest boundary value.

• Identifying the Outliers
• Resolving Inconsistencies

#2) Data Integration

When multiple heterogeneous data sources such as databases, data cubes or files are combined for analysis, this process is called data integration. This can help in improving the accuracy and speed of the data mining process.

Different databases have different naming conventions of variables, by causing redundancies in the databases. Additional Data Cleaning can be performed to remove the redundancies and inconsistencies from the data integration without affecting the reliability of data.

Data Integration can be performed using Data Migration Tools such as Oracle Data Service Integrator and Microsoft SQL etc.

#3) Data Reduction

This technique is applied to obtain relevant data for analysis from the collection of data. The size of the representation is much smaller in volume while maintaining integrity. Data Reduction is performed using methods such as Naive Bayes, Decision Trees, Neural network, etc.

Some strategies of data reduction are:

• Dimensionality Reduction: Reducing the number of attributes in the dataset.
• Numerosity Reduction: Replacing the original data volume by smaller forms of data representation.
• Data Compression: Compressed representation of the original data.

#4) Data Transformation

In this process, data is transformed into a form suitable for the data mining process. Data is consolidated so that the mining process is more efficient and the patterns are easier to understand. Data Transformation involves Data Mapping and code generation process.

Strategies for data transformation are:

• Smoothing: Removing noise from data using clustering, regression techniques, etc.
• Aggregation: Summary operations are applied to data.
• Normalization: Scaling of data to fall within a smaller range.
• Discretization: Raw values of numeric data are replaced by intervals. For Example, Age.

#5) Data Mining

Data Mining is a process to identify interesting patterns and knowledge from a large amount of data. In these steps, intelligent patterns are applied to extract the data patterns. The data is represented in the form of patterns and models are structured using classification and clustering techniques.

#6) Pattern Evaluation

This step involves identifying interesting patterns representing the knowledge based on interestingness measures. Data summarization and visualization methods are used to make the data understandable by the user.

#7) Knowledge Representation

Knowledge representation is a step where data visualization and knowledge representation tools are used to represent the mined data. Data is visualized in the form of reports, tables, etc.

Data mining Development in short (the following screenshot is from internet):