⛏️ Data Mining

 Data Mining is the process of extracting patterns and knowledge from large datasets. It finds hidden patterns in large databases and infers rules to predict future behavior.

In Data Mining, Data Reduction refers to techniques that reduce the volume of data while maintaining its integrity and analytical value. This helps in faster processing, storage efficiency, and better model performance.

Data Compression is a data reduction technique.

Dimension reduction methods (like Principal Component Analysis (PCA), Factor Analysis, etc.) aim to simplify high-dimensional data by leveraging the correlation structure among predictor variables.

Let’s examine each statement:

🔹 a. To reduce the number of predictor components

This is the main purpose of dimension reduction — reducing a large set of variables to a smaller set of representative components, while retaining the most important information.

🔹 b. To provide a framework for interpretability of the results

By simplifying the data into fewer dimensions, it becomes easier to interpret patterns, relationships, and clusters in the data.

🔹 c. To help ensure that these components are independent

Especially in PCA, the resulting principal components are orthogonal (i.e., uncorrelated), ensuring statistical independence.

🔹 d. To decrease the number of predictor components

The goal is to reduce the number of components.

✅ The correct sequence of steps to convert raw real-world data into minable datasets is:

1️⃣Data Consolidation – This is the first step where data is collected and integrated from multiple sources such as databases, flat files, sensors, etc., into a central repository (like a data warehouse or data lake).

2️⃣Data Cleaning – After consolidation, the data is often noisy or inconsistent. Data cleaning involves detecting and correcting errors or removing inaccurate records (e.g., missing values, duplicates, outliers).

3️⃣Data Transformation – Cleaned data is then transformed into suitable formats, like normalization, aggregation, generalization, or encoding. This step helps in structuring data appropriately for mining.

4️⃣Data Reduction – Finally, data is reduced in volume while preserving its integrity. Techniques include dimensionality reduction, data compression, or numerosity reduction to improve the efficiency of data mining.