Data Mining Concepts and Techniques
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Questions and Answers

What is a key difference between a data warehouse and an operational database?

  • A data warehouse requires constant data syncing, while an operational database does not.
  • A data warehouse primarily manages data in flat files, whereas an operational database uses relational tables.
  • A data warehouse is typically optimized for transactional processing, while an operational database supports analytical queries.
  • A data warehouse is designed for long-term data storage and analysis, while an operational database handles real-time data updates. (correct)

Which of the following best describes the role of the Apriori algorithm in data mining?

  • It is used for classification tasks by applying a decision tree framework.
  • It clusters data points based on similarity measurements.
  • It generates association rules from frequent itemsets by using a breadth-first search strategy. (correct)
  • It cleans data by removing invalid entries before analysis.

What is a significant challenge regarding user interaction in data mining?

  • Minimizing the computational speed required for mining processes.
  • Limiting the types of data formats that can be analyzed.
  • Ensuring that data mining methods can operate on small datasets.
  • Providing users with an intuitive interface that conveys complex mining results effectively. (correct)

What is a primary objective of data cleaning in the data mining process?

<p>To remove irrelevant data from the dataset. (A)</p> Signup and view all the answers

Which approach is NOT commonly associated with the process of data cleaning?

<p>Creating multiple copies of data for backup purposes. (D)</p> Signup and view all the answers

In market basket analysis, what is the main focus of the Apriori algorithm?

<p>To determine frequent item sets based on transaction data. (C)</p> Signup and view all the answers

What role do data mining primitives play in data mining tasks?

<p>They define the parameters and functionalities to set up a data mining task. (D)</p> Signup and view all the answers

Which of the following distinguishes classification from clustering methods?

<p>Classification is a supervised learning technique, clustering is unsupervised. (B), Classification requires labeled data, whereas clustering does not. (C)</p> Signup and view all the answers

What is the significance of the minimum support threshold in the Apriori algorithm?

<p>It governs which item sets are considered frequent based on their occurrence in transactions. (D)</p> Signup and view all the answers

In the context of OLAP operations, which of the following best describes 'drill down'?

<p>Increasing the granularity of data by providing more detailed data. (A)</p> Signup and view all the answers

What is one key difference between Operational Database Systems and Data Warehouses?

<p>Operational databases are optimized for transaction processing, whereas data warehouses are designed for analysis and reporting. (D)</p> Signup and view all the answers

Which method is NOT commonly used for generating frequent item sets in data mining?

<p>Depth-first iterative deepening (C)</p> Signup and view all the answers

Which of the following best describes the role of data cleaning in data processing?

<p>Data cleaning involves correcting or removing erroneous data to ensure accuracy. (B)</p> Signup and view all the answers

What is a characteristic feature of the Apriori algorithm in frequent item set generation?

<p>It employs a bottom-up approach, extending small item sets to larger ones. (A)</p> Signup and view all the answers

Which of the following is an example of a multilevel association rule?

<p>Customers who purchase laptops will often purchase peripherals at a later date. (C)</p> Signup and view all the answers

What is a primary characteristic that distinguishes an operational database from a data warehouse?

<p>Operational databases support normalizes data organization. (A)</p> Signup and view all the answers

Which of the following is NOT a step involved in Knowledge Discovery?

<p>User Classification (B)</p> Signup and view all the answers

In the context of data cleaning, which activity is primarily focused on correcting inconsistencies in the dataset?

<p>Data Validation (B)</p> Signup and view all the answers

What is the minimum support threshold for the Apriori algorithm given in the problem statement?

<p>20% (B)</p> Signup and view all the answers

Which of the following clustering methods is specifically based on the distance between points?

<p>K-Means Clustering (D)</p> Signup and view all the answers

What best describes the concept of multilevel association rules?

<p>Rules that represent associations at different levels of abstraction. (A)</p> Signup and view all the answers

Which statement regarding outlier analysis is true?

<p>Outlier analysis helps identify rare events that deviate significantly from majority data. (C)</p> Signup and view all the answers

In the context of prediction analysis, what is its primary purpose?

<p>To make informed guesses about future events or trends. (D)</p> Signup and view all the answers

Flashcards

Data Mining and Knowledge Discovery

The process of extracting valuable insights and patterns from large datasets.

Data Warehouse

A centralized repository of integrated data from various sources, optimized for analytical processing.

Data Cleaning

The process of identifying and correcting errors, inconsistencies, and inaccuracies in data.

Constraint-Based Association Mining

A method of finding relationships between data items based on specified user-defined constraints.

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Clustering

Grouping similar data points together based on their characteristics.

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Data Warehouse Architecture

A layered structure storing data from various operational systems for analytical processing.

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Data Cleaning Activities

Processes to identify and correct errors, inconsistencies, and inaccuracies in data.

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Apriori Algorithm

A frequent itemset mining algorithm that finds frequent itemsets based on prior knowledge of frequent itemsets.

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Operational Database vs. Data Warehouse

Operational databases focus on daily transactions, while Data Warehouses store historical data for analysis.

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Frequent Itemsets

Items that occur together frequently in a database, representing relationships.

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Data Mining

The process of extracting meaningful patterns and insights from large datasets.

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OLAP Operations

Operations used to analyze data in a multidimensional data cube, allowing for slicing, dicing, and drilling down into data.

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Market Basket Analysis

Analyzing customer purchase patterns to identify common items bought together, like bread and butter.

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Naïve Bayesian Classification

A probabilistic classification algorithm based on Bayes' theorem, assigning probabilities to class memberships.

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Data Cube

A multidimensional data structure that allows efficient storage and analysis of data, providing a way to explore data from different perspectives.

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Data Cube Schema

The structure of a data cube, defining its dimensions, measures, and how data is aggregated.

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Star Schema

A data cube schema where a central fact table joins with multiple dimension tables, each representing a specific aspect of the data.

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Snowflake Schema

A hierarchical data cube schema where dimension tables can be further normalized, creating a snowflake-like structure.

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OLAP

Online Analytical Processing, a technique for analyzing multidimensional data, allowing for efficient querying and exploration.

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Support (Association Rule)

The proportion of transactions in a database that contain a specific itemset, indicating its popularity.

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Confidence (Association Rule)

The probability that a transaction containing a specific itemset also contains another specified itemset, indicating the strength of the relationship.

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Concept and Class Description

A concise representation of the characteristics and properties of a specific concept or class, summarizing the key features of a group of data points.

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Study Notes

MCA-301 Data Mining (May 2024)

  • Examination: November 2022
  • Time: Three Hours
  • Maximum Marks: 70
  • Attempt any five questions
  • All questions carry equal marks
  • In case of any doubt or dispute the English version question should be treated as final

Question 1

a) Define Data mining and knowledge discovery? Explain how the evolution of database technology led to data mining?

  • Discuss major issues in data mining regarding mining methodologies, user interaction, performance and diverse data types

b) Describe various data mining primitives for specifying a data mining task.

Question 2

a) Give the differences between Operational Database Systems and Data Warehouse.

  • Explain the different steps involved in knowledge discovery in data mining.
  • Discuss the various steps involved in knowledge discovery, from data cleaning to prediction

b) Explain the different steps involved in knowledge discovery using a diagram

Question 3

a) What is Data Warehousing? How it is different from an Operational Database? Write the advantages of Data Warehouse

  • What do you understand by "data cleaning as a process"? Give the approaches for data cleaning.

b) Discuss the activities of data cleaning with processes associated with it.

Question 4

a) Explain constraint-based association mining.

  • Explain constraint-based association mining with an example

b) Explain Apriori algorithm. Give few techniques to improve the efficiency of Apriori algorithm.

Question 5

a) Describe the partitioning and density-based methods of clustering. Write applications of clustering.

  • Describe the partitioning and density-based methods of clustering. Write applications of clustering

b) Write about data mining currently available tools.

Question 6

a) Write about Data Warehouse Architecture and implementation.

  • Explain Navies Bayesian classification with example

b) Give the different between classification and clustering methods.

Question 7

a) Write down the OLAP operations.

  • Explain various alternative methods for generating frequent item sets

b) Explain data transformations.

  • Explain different clustering methods with suitable examples.

Question 8

a) Write on Generating Association rules from Frequent Items.

  • Explain outlier analysis with example

b) What is Prediction? Explain the need of predictive analysis in data mining.

  • Explain decision tree induction with example

c) Write short notes on the following

  • OLAP
  • Support and confidence
  • Concept and class description (with respective methods)
  • Clustering

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Description

Test your knowledge on key concepts in data mining, including data warehousing, the Apriori algorithm, and data cleaning. This quiz covers various aspects of data mining techniques and their applications in market analysis and OLAP operations.

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