**Data Mining Notes for Students PDF**

*Date: 30th Jan 2023*

In these “** Data Mining Notes for Students PDF**”, we will introduce data mining techniques and enables you to apply these techniques on real-life datasets. These notes focus on three main data mining techniques: Classification, Clustering, and Association Rule Mining tasks.

We have provided multiple complete Data Mining Notes for Btech for any university student of BCA, MCA, B.Sc, B.Tech CSE, M.Tech branch to enhance more knowledge about the subject and to score better marks in the exam. Students can easily make use of all these Data Mining Notes for Btech by downloading them.

**Topics in our Data Mining Handwritten Notes PDF**

The topics we will cover in these **Data Mining Handwritten Notes PDF** will be taken from the following list:

**Introduction to Data Mining: **Applications of data mining, data mining tasks, motivation and challenges, types of data attributes and measurements, data quality.

**Data Pre-Processing: **Aggregation, sampling, dimensionality reduction, Feature Subset Selection, Feature Creation, Discretization and Binarization, Variable Transformation.

**Classification: **Basic Concepts, Decision Tree Classifier: Decision tree algorithm, attribute selection measures, Nearest Neighbour Classifier, Bayes Theorem, and Naive Bayes Classifier,

**Model Evaluation: **Holdout Method, Random Sub Sampling, Cross-Validation, evaluation metrics, confusion matrix.

**Association rule mining:** Transaction data-set, Frequent Itemset, Support measure, Apriori Principle, Apriori Algorithm, Computational Complexity, Rule Generation, Confidence of association rule.

**Cluster Analysis: **Basic Concepts, Different Types of Clustering Methods, Different Types of Clusters

**K-means:** The Basic K-means Algorithm, Strengths and Weaknesses of K-means algorithm

**Agglomerative Hierarchical Clustering:** Basic Algorithm, Proximity between clusters

**DBSCAN:** The DBSCAN Algorithm, Strengths, and Weaknesses.

**Download Data Mining Notes for BTech**

**Data Mining Books**

We have listed the best Data Mining Books that can help in your Data Mining exam preparation:

**Data Mining Handwritten Notes PDF FAQs**

## What is Data Mining ?

Data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data.

## What does data mining mean ?

It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

## What are the key properties of Data Mining ?

1. Automatic discovery of patterns

2. Prediction of likely outcomes

3. Creation of actionable information

4. Focus on large datasets and databases

## What are the tasks of Data Mining ?

Data mining involves six common classes of tasks:

1. Anomaly detection (Outlier/change/deviation detection)

2. Association rule learning (Dependency modelling)

3. Clustering

4. Classification

5. Regression

6. Summarization

**Computer Science Notes**

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