MCA-110 Data Warehousing and Data Mining Notes cover all four units of the GGSIPU MCA Semester 2 Core Elective I paper in 75 pages, with 14 solved numericals, 19 figures and 60 exam-style questions. They follow the MCA syllabus for affiliated institutes that applies from academic year 2025-26 onwards.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Ritik Rawat, B.Tech, Senior Full Stack Developer · Checked against the MCA Scheme of Examination and Detailed Syllabus (affiliated institutes, 1 August 2025) · Updated 13 October 2026 · Editorial policy
Scheme: MCA (2 years), affiliated institutes, A.S. 2025-26 onwards | Applies to: academic year 2025-26 onwards | Verified against: MCA Scheme of Examination and Detailed Syllabus, 1 August 2025
MCA-110 Data Warehousing and Data Mining at a glance: GGSIPU MCA Sem 2 credits, units and exam pattern
MCA-110 is one of ten Core Elective I options; each student takes one. Values are from the official MCA syllabus.
| Code and title | MCA-110 Data Warehousing and Data Mining |
|---|---|
| Course and semester | MCA (2 years), Semester 2 |
| Category and credits | Core Elective I; 4 credits (3 lecture + 1 tutorial hour a week) |
| Marks | Out of 100, pass mark 40 (Important Note 9); end-term paper 60; internal split not printed in the syllabus PDF |
| End-term pattern | Q1 compulsory, short answers from the whole syllabus, 12 marks; then one of two questions per unit, 12 marks each (12 + 48 = 60 marks) |
| Units | 4 (12, 10, 12 and 10 teaching hours) |
| Pre-requisites | Information System Concepts; Database Management System |
| Rules | GGSIPU Ordinance 11 |
| Paired lab | MCA-168 Lab based on Core Elective I: 2 practical hours, 1 credit |
Core papers and labs of this semester are on GGSIPU MCA Semester 2 papers.
MCA-110 Data Warehousing and Data Mining Notes unit-wise: 14 solved numericals and 19 figures across 4 units
Each unit ends with an exam corner (definitions, key terms, questions, revision table). The official PDF numbers units only, so the titles below are its topic headings. Notes for the other papers are under GGSIPU MCA notes; one page per unit is in the sample PDF above.
MCA-110 Unit 1: Data Warehouse and OLAP Technology
Inmon's four features, OLTP versus warehouse, the six building blocks, warehouse models, metadata and the three-tier architecture, then fact and dimension tables, star, snowflake and constellation schemas, the cuboid lattice, five OLAP operations, ROLAP/MOLAP/HOLAP and a retail-chain case study.
Notes: pages 4-24 · 13 sections · 6 figures · 1 worked example + 1 case study · 15 exam questions.
| Operation | Result |
|---|---|
| Roll-up city to state (Haryana) | Mobile 850, Laptop 570, TV 480 |
| Slice item = Laptop | Delhi 605, Gurugram 380, Faridabad 190 |
| Dice Delhi/Gurugram × Mobile/TV | 820, 410, 540, 220 |
| Pivot | Items as rows, cities as columns |
Quick look: OLAP operations on Q1 sales, ₹ thousand (from page 19)
- Operation
- Roll-up city to state (Haryana)
- Result
- Mobile 850, Laptop 570, TV 480
- Operation
- Slice item = Laptop
- Result
- Delhi 605, Gurugram 380, Faridabad 190
- Operation
- Dice Delhi/Gurugram × Mobile/TV
- Result
- 820, 410, 540, 220
- Operation
- Pivot
- Result
- Items as rows, cities as columns
MCA-110 Unit 2: Introduction to Data Mining and Data Pre-Processing
Two definitions of data mining, six functionalities, applications, five groups of issues, task primitives with a DMQL query, then attribute types, data quality and the four pre-processing tasks: cleaning (binning), integration (χ² test), transformation (normalisation) and reduction (PCA, sampling).
Notes: pages 25-41 · 13 sections · 4 figures · 5 solved numericals · 15 exam questions.
| Bin | By means | By boundaries |
|---|---|---|
| 4, 8, 15 | 9, 9, 9 | 4, 4, 15 |
| 21, 21, 24 | 22, 22, 22 | 21, 21, 24 |
| 25, 28, 34 | 29, 29, 29 | 25, 25, 34 |
Quick look: smoothing prices 4 to 34 in bins of depth 3 (from page 35)
- Bin
- 4, 8, 15
- By means
- 9, 9, 9
- By boundaries
- 4, 4, 15
- Bin
- 21, 21, 24
- By means
- 22, 22, 22
- By boundaries
- 21, 21, 24
- Bin
- 25, 28, 34
- By means
- 29, 29, 29
- By boundaries
- 25, 25, 34
MCA-110 Unit 3: Classification and Association Rule Mining
Supervised versus unsupervised learning, decision trees (information gain, gain ratio, Gini, pruning), naïve Bayes, backpropagation and model evaluation, then support, confidence, lift, Apriori and FP-Growth.
Notes: pages 42-59 · 11 sections · 4 figures · 5 solved numericals · 15 exam questions.
| Rule | Confidence | Strong? |
|---|---|---|
| I1 ∧ I2 ⇒ I5 | 2/4 = 50% | No |
| I1 ∧ I5 ⇒ I2 | 2/2 = 100% | Yes |
| I5 ⇒ I1 ∧ I2 | 2/2 = 100%, lift 2.25 | Yes |
Quick look: rules from {I1, I2, I5} at 70% min confidence (from page 55)
- Rule
- I1 ∧ I2 ⇒ I5
- Confidence
- 2/4 = 50%
- Strong?
- No
- Rule
- I1 ∧ I5 ⇒ I2
- Confidence
- 2/2 = 100%
- Strong?
- Yes
- Rule
- I5 ⇒ I1 ∧ I2
- Confidence
- 2/2 = 100%, lift 2.25
- Strong?
- Yes
MCA-110 Unit 4: Cluster Analysis and Outlier Analysis
Distance measures, k-means, k-medoids, AGNES, DIANA, BIRCH, DBSCAN, cluster evaluation (elbow, silhouette), then global, contextual and collective outliers and four detection families, including LOF.
Notes: pages 60-75 · 10 sections · 5 figures · 3 solved numericals · 15 exam questions.
Quick look: single-link clustering of five points (from page 66)
- Distance 0.71 merges P1 and P2.
- Distance 1.00 merges P4 and P5.
- P3 joins {P4, P5} at min(2.24, 1.41) = 1.41.
- Cutting the dendrogram at 2.0 leaves {P1, P2} and {P3, P4, P5}.
The KDD process in data mining: excerpt from page 31 of the MCA-110 notes
Excerpt from page 31
2.6 KDD process (knowledge discovery in databases)
Many people treat data mining as another name for knowledge discovery in databases (KDD). Others see data mining as only one essential step inside a longer discovery process. KDD is an iterative sequence of seven steps:
- Data cleaning: remove noise and inconsistent data.
- Data integration: combine multiple data sources.
- Data selection: retrieve the data relevant to the analysis task from the database.
- Data transformation: transform and consolidate the data into forms suitable for mining, for example through summary or aggregation operations.
- Data mining: the essential step, where intelligent methods are applied to extract patterns.
- Pattern evaluation: identify the truly interesting patterns that represent knowledge, using interestingness measures.
- Knowledge presentation: visualisation and knowledge representation techniques present the mined knowledge to users.
How the steps fit together
- Steps 1 to 4 are different forms of data pre-processing: they prepare the data for mining.
- The mining step may interact with the user or with a knowledge base. Interesting patterns are shown to the user and may be stored as new knowledge.
- The process is iterative. After evaluation, the user may go back to an earlier step, for example to select different data or change a threshold.
Key point
KDD is the overall process and data mining is one step in it. If the exam asks "Is data mining the same as KDD?", write both views and draw the KDD diagram (Fig. 2.2 in the notes shows data mining as one step of the process).
2.7 OLAP vs data mining
Both OLAP and data mining work on warehouse data, but they answer different kinds of questions. OLAP is user-driven: the analyst forms a hypothesis and verifies it. Data mining is data-driven: the system discovers patterns the user did not know to look for.
How to write MCA-110 answers: schema diagrams, step-wise numericals and case-study structure
The MCA-110 Data Warehousing and Data Mining IPU notes train three answer types: definition-and-diagram theory, step-wise numericals and an OLAP case study.
- Theory. Open with a quoted definition (each exam corner gives three, from Inmon, the OLAP Council or Han, Kamber and Pei), draw the diagram, then write four or five points per part. For warehouse architecture: Fig. 1.1 with three labelled tiers, the back-end functions table and one closing line on metadata. Memory aids in the notes: SITN for the four warehouse features, MUEDS for the five groups of data mining issues.
- Numericals. Formula first, then substitution, then the result; step marks count even if the arithmetic slips. For Apriori, state min_sup and min_conf and show Ck, the prune step and Lk at every pass before listing strong rules. For FP-Growth, draw the tree with its header table and the conditional pattern base table.
- Case study. Five parts: problem, dimensions and measures, schema diagram, one OLAP query per operation, benefits. The same frame fits a bank, hospital or university.
Worked example: counting cuboids (page 17)
Cuboids in a 3-D cube whose dimensions have 3, 4 and 2 hierarchy levels? Use ∏(Li + 1), the +1 being the top level "all": 4 × 5 × 3 = 60 cuboids. Without hierarchies it is 2n.
Common slips. Treating noise as outliers (noise is removed, outliers are studied); trusting a strong rule without lift (games and videos: 66.7% confidence, lift 0.89); and forgetting that k-means needs k in advance.
Marks sizing. The notes size answers for 2.5 and 12.5 marks; the 2025-26 pattern gives Q1 12 marks in all and each unit question 12, so keep the structure and trim the length.
Practise this structure on GGSIPU MCA important questions.
MCA-110 exam corner: 36 short-answer and 24 long-answer questions by unit
Every MCA-110 unit ends with Q1-type short questions and long unit questions, answered in that unit's sections.
| Unit | Short | Long | Solved numericals |
|---|---|---|---|
| I. Warehouse and OLAP | 9 | 6 | 1 + case study |
| II. Mining and pre-processing | 9 | 6 | 5 |
| III. Classification and association | 9 | 6 | 5 |
| IV. Clustering and outliers | 9 | 6 | 3 |
MCA-110 questions and solved numericals by unit
- Unit
- I. Warehouse and OLAP
- Short
- 9
- Long
- 6
- Solved numericals
- 1 + case study
- Unit
- II. Mining and pre-processing
- Short
- 9
- Long
- 6
- Solved numericals
- 5
- Unit
- III. Classification and association
- Short
- 9
- Long
- 6
- Solved numericals
- 5
- Unit
- IV. Clustering and outliers
- Short
- 9
- Long
- 6
- Solved numericals
- 3
The MCA-110 notes list 36 short-answer and 24 long-answer questions across 4 units, plus 14 solved numericals.
- Unit I: Differentiate ROLAP and MOLAP. Pointer: ROLAP uses relational tables and scales well; MOLAP uses pre-computed array cubes and answers fastest (page 20).
- Unit II: Normalise 73,600 by min-max (min 12,000, max 98,000) to [0, 1]. Pointer: 61,600 / 86,000 = 0.716 (page 37).
- Unit III: State the Apriori property. Pointer: every non-empty subset of a frequent itemset is frequent, so supersets of infrequent itemsets are pruned (page 53).
- Unit IV: Differentiate noise and outliers. Pointer: noise is random error to remove; an outlier comes from a different mechanism and is studied (page 69).
More practice sits in the MCA-110 important questions.
MCA-110 and the MCA-168 data warehousing and data mining lab, plus other GGSIPU MCA Sem 2 papers
Paired lab: MCA-168 Data Warehousing and Data Mining lab notes · 2 practical hours, 1 credit · four course outcomes printed, no practical list.
- Unit I (cube, roll-up, slice, dice) feeds lab CO1.
- Unit II (binning, normalisation) feeds CO2, transforming noisy data.
- Units III and IV (decision trees, Apriori, k-means, DBSCAN) feed CO3 and CO4, applying and choosing mining algorithms.
Sem 2 papers alongside MCA-110: MCA-102 Data and File Structures, MCA-104 Object Oriented Software Engineering, MCA-106 Python Programming, and one Open Elective I (MCA-128 Digital Marketing, MCA-130 Management Information System, MCA-132 Management Principles and Organizational Behavior, MCA-134 Finance and Accounting). The pre-requisite DBMS is MCA-107 in Semester 1.
Who writes and checks the MCA-110 DWDM notes?
- Prateek Talwar, author: Founder, Unnati Educations; Associate Company Secretary (ACS), ICSI, membership no. 58704, since 15 May 2019; B.Com, University of Delhi (School of Open Learning), 2012. Profile
- Ritik Rawat, subject reviewer: Senior Full Stack Developer, Unnati Educations · B.Tech · checks the OLAP, database and algorithm content.
These GGSIPU MCA Sem 2 Data Warehousing and Data Mining notes are mapped to the affiliated-institutes MCA scheme of 1 August 2025. The 2026-27 edition has 47 sections and 136 quick revision rows; updated 13 October 2026 under our editorial policy.
The same team prepares GGSIPU important questions in the MCA-110 unit order.
Do the MCA-110 notes cover the full GGSIPU syllabus?
Yes. Every topic in the four official MCA-110 units has a matching section in the notes; no gaps.
| Unit | Official topics | In the notes | Status |
|---|---|---|---|
| I | Data warehouse definition, database vs data warehouse, need, building blocks, models, metadata, architecture, dimension and fact tables; star, snowflake, constellation; data cube and OLAP operations; ROLAP, MOLAP, HOLAP; OLAP case study | 1.1-1.13, pp 5-21 | Covered |
| II | Definition and functionalities, applications, issues, primitives, KDD, OLAP vs data mining; data objects and attributes, inconsistent data, need for pre-processing; cleaning, integration, transformation, reduction | 2.1-2.13, pp 26-38 | Covered |
| III | Supervised and unsupervised learning; classification and prediction, issues; decision tree, naïve Bayesian, backpropagation, model evaluation; association rules, frequent itemsets, Apriori, FP Growth, support, confidence, lift | 3.1-3.11, pp 43-57 | Covered |
| IV | Applications of clustering; partitioning, hierarchical, density based; evaluation of clusters; types of outliers, challenges, detection methods | 4.1-4.10, pp 61-73 | Covered |
MCA-110 official syllabus mapped to the notes
- Unit
- I
- Official topics
- Data warehouse definition, database vs data warehouse, need, building blocks, models, metadata, architecture, dimension and fact tables; star, snowflake, constellation; data cube and OLAP operations; ROLAP, MOLAP, HOLAP; OLAP case study
- In the notes
- 1.1-1.13, pp 5-21
- Status
- Covered
- Unit
- II
- Official topics
- Definition and functionalities, applications, issues, primitives, KDD, OLAP vs data mining; data objects and attributes, inconsistent data, need for pre-processing; cleaning, integration, transformation, reduction
- In the notes
- 2.1-2.13, pp 26-38
- Status
- Covered
- Unit
- III
- Official topics
- Supervised and unsupervised learning; classification and prediction, issues; decision tree, naïve Bayesian, backpropagation, model evaluation; association rules, frequent itemsets, Apriori, FP Growth, support, confidence, lift
- In the notes
- 3.1-3.11, pp 43-57
- Status
- Covered
- Unit
- IV
- Official topics
- Applications of clustering; partitioning, hierarchical, density based; evaluation of clusters; types of outliers, challenges, detection methods
- In the notes
- 4.1-4.10, pp 61-73
- Status
- Covered
Official source: MCA Scheme of Examination and Detailed Syllabus, A.S. 2025-26 onwards.
MCA-110's Semester 2 siblings get the same unit check before they join GGSIPU notes.
MCA-110 DWDM notes FAQs
How do I get the full MCA-110 notes?
The full MCA-110 notes are sold as a printed book only; they are not available as a PDF. Tap Get Full Notes to message 9899436384 on WhatsApp, and the book is delivered within 5 to 7 days at most, depending on your location. View Sample PDF shows watermarked pages, one per unit, to check the style first.
Is MCA-110 compulsory in MCA Semester 2?
No. MCA-110 is one of ten Core Elective I options, MCA-108 to MCA-126, and each student takes one. The scheme says an elective needs at least 20 students for classes; with fewer, it may still be offered, but students complete it through a MOOC platform such as SWAYAM with prior approval of the Programme Co-ordination Committee.
What is the passing mark for MCA-110?
MCA-110 is marked out of 100, and the end-term paper carries 60 of those marks (12 for Q1 plus 12 for each of four unit questions). The MCA scheme of examination states pass marks as 40%, which is 40 out of 100, for every paper. For end-term minimums and re-appear rules, see Ordinance 11 on ipu.ac.in.
Which MCA-110 unit has the most solved numericals?
Units II and III have five each. Unit II solves binning, a χ² test and min-max, z-score and decimal-scaling normalisation. Unit III solves an ID3 decision tree, naïve Bayes, a cancer-test confusion matrix, Apriori and FP-Growth on nine transactions. Unit IV has three (k-means, single-link dendrogram, IQR outliers) and Unit I one OLAP example plus a case study.
Do the MCA-110 notes follow the 2025-26 MCA syllabus?
Yes. The MCA-110 notes follow the four units of the MCA syllabus for affiliated institutes that applies from academic year 2025-26, and every official topic has a section. One difference: the exam corners label questions 2.5 and 12.5 marks, while the 2025-26 pattern gives Q1 12 marks in total and each unit question 12 marks.
Is there a practical lab with MCA-110?
Yes. Students who pick MCA-110 take MCA-168, the Lab based on Core Elective I, in its data warehousing and data mining form: 2 practical hours a week and 1 credit. The syllabus lists four lab outcomes, from OLAP operations on multidimensional data to choosing a suitable mining algorithm, but prints no list of practicals for this lab.
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