---
title: "Section 2 · Data Mining Lab (WEKA)"
description: "Ten WEKA sessions: datasets and preprocessing, Apriori and FP-Growth association rules, classification with decision trees, Naive Bayes, k-NN and SVM, regression, and k-means, hierarchical and density-based clustering."
image: "https://syntax.theether.in/og.png"
---

> Documentation Index
> Fetch the complete documentation index at: https://syntax.theether.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Section 2 · Data Mining Lab (WEKA)

Ten WEKA sessions: datasets and preprocessing, Apriori and FP-Growth association rules, classification with decision trees, Naive Bayes, k-NN and SVM, regression, and k-means, hierarchical and density-based clustering.

> **Question paper**
>
> All exercises of this section as one typed paper: [view online](/question-papers/mcsl-223-section-2) or <a href={"/downloads/mcsl-223-section-2/mcsl-223-section-2-questions.pdf"}>download the PDF</a>. Session and question numbers here match it.

## Objectives

- Handle data mining tasks using a data mining toolkit such as open source WEKA
- Understand the various kinds of algorithms available in WEKA
- Understand the data sets and data preprocessing
- Demonstrate classification, clustering and association rule mining in large data sets
- Exercise the data mining techniques with varied input values for different parameters
- Apply mining techniques for realistic data

## Sessions

| Session | Topic | Questions |
| --- | --- | --- |
| [Session 1](/mcsl-223/section-2/session-1) | WEKA, datasets and ARFF files | Q1 to Q4 |
| [Session 2](/mcsl-223/section-2/session-2) | Basic preprocessing | Q5 to Q6 |
| [Session 3](/mcsl-223/section-2/session-3) | Unsupervised filters and Apriori | Q7 to Q8 |
| [Session 4](/mcsl-223/section-2/session-4) | FP-Growth and Apriori parameter studies | Q9 to Q11 |
| [Session 5](/mcsl-223/section-2/session-5) | Reading Apriori output | Q12 |
| [Session 6](/mcsl-223/section-2/session-6) | Apriori on the zoo dataset and regression | Q13 to Q14 |
| [Session 7](/mcsl-223/section-2/session-7) | Classification: logistic regression, decision tree, Naive Bayes, k-NN, SVM | Q15 to Q16 |
| [Session 8](/mcsl-223/section-2/session-8) | Decision trees, rules, entropy, kappa and ROC | Q17 to Q18 |
| [Session 9](/mcsl-223/section-2/session-9) | k-means clustering | Q19 to Q21 |
| [Session 10](/mcsl-223/section-2/session-10) | Hierarchical and density-based clustering | Q22 to Q23 |

## Before the first session

- Install WEKA 3.8 or later (Java bundled). The sample datasets (weather, iris, glass, contact-lenses, labor, zoo, bank, credit) are in the `data` folder of the installation; `student.arff` and `employee.arff` you create yourself in Session 1.
- For every run, record the exact parameter values, the run information block, and the numbers the formula sheet lets you check by hand (support, confidence, entropy, accuracy, kappa, SSE).
- Screenshots go in the record only when the numbers are also written out; the evaluator marks the interpretation, not the picture.

Source: https://syntax.theether.in/mcsl-223/section-2/index.mdx