What is Data Mining?
Data mining is the practice of delving into vast reserves of information to unearth hidden patterns and relationships. These patterns, often invisible to the naked eye, offer valuable insights that can revolutionize decision-making across diverse fields. Through sophisticated algorithms and statistical techniques, data mining acts as a powerful tool for extracting knowledge from the raw data, bridging the gap between information and understanding.
Data Preprocessing Steps
Scenario. Analyzing customer purchase data to identify buying trends
Data Collection
Data is collected from heterogeneous sources. Now, this step takes care of how data is collected from various sources that are relevant to the specific work. There are two scenarios in which data gets collected. First is when an expert controls the data generation process, which is well designed and understood. Second, when experts cannot influence the data generation process, an observational approach is used to randomly generate data.
Gather customer transaction data from various sources in your organization, such as point-of-sale systems, online stores, and customer relationship management (CRM) software. This data might include:
- Customer ID
- Product ID
- Quantity purchased
- Transaction date
- Price paid
- Discount applied (if any)
- Customer demographics (optional)
- Location (optional)
Data Selection
Data selection is defined as the process where data relevant to the analysis is decided and retrieved from the data collection. The primary objective of data selection is determining the appropriate data type, source, and instrument.
The two primary data types and sources are:
- Quantitative represents as numerical figures.
- Qualitative are text, images, audio/video, etc.
Example. Focus on the relevant attributes for your analysis. In this case, you might select:
- Customer ID
- Product ID
- Quantity purchased
- Transaction date
Data Integration
Data Integration is the combining of all the data collected from heterogeneous sources and selected as required. It is a strategy that integrates data from several sources to make it available to users in a single uniform view that shows their status.
There are mainly two kinds of approaches to data integration in data mining they are-
- Tight Coupling
- Loose Coupling
Example. If the data comes from different sources, combine it into a single, unified dataset. Ensure consistent formatting and data types (e.g., all dates in the same format) across all sources. Tools like Extract, Transform, and Load (ETL) can be used for this purpose.
Data Cleaning
Data Cleaning is also referred to as Data Cleansing. Data Cleaning is the process of filling in the missing values, smoothing noisy data, removing inconsistencies, and analyzing and removing outliers that lead to correcting data and removing all kinds of errors from the dataset. It’s a very critical step in the data mining process as it ensures accuracy and consistency and even improves the quality of the analysis.
Steps for data cleaning in the data mining process can vary, but some common steps are:
- Data profiling
- Handling missing data
- Handling duplicates
- Handling Outliers
- Standardization
- Resolving inconsistencies
- Quality assurance
Example. Identify and address errors or inconsistencies in the data. This might involve:
- Handling missing values: Impute missing values (estimate them) or remove rows with too many missing entries.
- Correcting typos or inconsistencies in product names or customer IDs.
- Dealing with outliers: Investigate extreme values (very high or low purchases) to determine if they are genuine or errors. You might decide to keep, adjust, or remove outliers.
Data Transformation
Data Transformation is used to convert the raw data into a suitable format that eases the data mining process. Data transformation includes data cleaning techniques and a data reduction technique to convert the data into the appropriate form.
Data Transformation involves several techniques. They are-
- Data Smoothing
- Attribute Construction
- Data Generalization
- Data Aggregation
- Data Discretization
- Data Normalization
Example. Convert data into a format suitable for analysis. This could include:
- Creating new attributes: Calculate total spending per customer or product category (e.g., group similar products together).
- Encoding categorical attributes: Convert text-based data like product categories into numerical codes for easier analysis by machine learning algorithms.
- Normalizing or standardizing numerical attributes: Scale values to a common range to prevent certain attributes from dominating the analysis.