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Data Mining MCQs - Unit-5
1. Web mining is the process of discovering useful information from ______.
A . Databases
B . Data warehouses
C . World Wide Web
D . Data lakes
2. The three main categories of web mining are ______.
A . Web structure, Web content, Web usage
B . Web log, Web page, Web link
C . Text, Image, Audio
D . Structured, Semi-structured, Unstructured
3. The WWW is a ______ information service center.
A . Local
B . Private
C . Distributed and global
D . Centralized
4. The main source of noise in web data comes from ______.
A . Advertisements and links
B . Audio files
C . Duplicate text
D . Encrypted data
5. Web content mining extracts information from ______.
A . Web hyperlinks
B . Web logs
C . Web page contents
D . IP addresses
6. NLP and IR techniques are primarily used in ______ mining.
A . Web usage
B . Web content
C . Web structure
D . Multimedia
7. Web structure mining studies ______ between web pages.
A . Content
B . Relationships
C . Size
D . Tags
8. The Google search engine uses which algorithm for ranking pages?
A . TF-IDF
B . PageRank
C . K-Means
D . Naïve Bayes
9. Web usage mining focuses on analyzing data from ______.
A . Server logs
B . Images
C . Hyperlinks
D . Audio files
10. The biggest ethical issue in web usage mining is ______.
A . Low accuracy
B . Privacy invasion
C . Data duplication
D . Speed of computation
11. Multimedia data mining deals with ______ types of data.
A . Structured
B . Text only
C . Multimedia
D . Numeric
12. Examples of multimedia data include ______.
A . Text and numbers
B . Images, audio, video
C . Tables and charts
D . Links and addresses
13. The two categories of multimedia data mining are ______.
A . Text and Image
B . Static and Dynamic media
C . Structured and Unstructured
D . Audio and Video
14. Text mining is also known as ______.
A . Text extraction
B . Text data mining
C . Text compression
D . Text visualization
15. The main goal of text mining is to extract ______ information from text.
A . Meaningful
B . Numeric
C . Graphical
D . Encrypted
16. The process of splitting text into words is called ______.
A . Segmentation
B . Lemmatization
C . Tokenization
D . Filtering
17. Lemmatization converts words to their ______.
A . Uppercase
B . Base or root form
C . Numeric form
D . Plural form
18. The Bag of Words (BOW) model represents text as ______.
A . Tables
B . Vectors
C . Trees
D . Matrices of images
19. TF-IDF stands for ______.
A . Term Frequency – Inverse Data Formula
B . Term Frequency – Inverse Document Frequency
C . Text Frequency – Internal Document Factor
D . Token Frequency – Input Document Factor
20. Applications of text mining include ______.
A . Weather prediction
B . Resume filtering and medical analysis
C . Image recognition
D . Video compression
Fill in the Blanks
21. Web mining uses automated methods to extract both structured and ______ data.
22. The process of removing HTML tags and advertisements is called ______.
23. The three main steps in web mining are data collection, preprocessing, and ______.
24. Web structure mining focuses on discovering the ______ between web pages.
25. Web usage mining helps analyze user behavior through ______.
26. Multimedia data mining extracts patterns from multimedia ______.
27. ________________________is the process of deriving high quality information from Text.
28. Text mining uses ______ to allow machines to understand human language.
29. The process of removing non-essential words like “is,” “and,” “the” is called ______.
30. NLP stands for _______________________________________________
31. The Bag of Words model ignores the ______ of words in a document.
32. Feature extraction in text mining converts text into ______ form.
Relevant Materials :
☞ Data Mining MCQs - Unit-1 - [ DM ]
☞ Data Mining MCQs - Unit-2 - [ DM ]
☞ Data Mining MCQs - Unit-3 - [ DM ]
☞ Data Mining MCQs - Unit-4 - [ DM ]
☞ Data Mining MCQs - Unit-5 - [ DM ]
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