DATA MINING
Data Mining Mastery Quiz
Test your knowledge on the fascinating field of Data Mining! This quiz covers various topics including natural language processing, information retrieval, and anomaly detection. Perfect for both students and professionals in the field, it comprises 41 multiple-choice questions designed to challenge your understanding and application of data mining concepts.
- 41 Engaging Questions
- Focus on Key Data Mining Topics
- Assess Your Knowledge and Skills
Natural language processing and information extraction
Text mining
Web Data
Natural Language Processing
Lexical analysis
Syntactic analysis
Semantic analysis
Pragmatic analysis
Influence
Word analysis
“design” can be a noun or a verb (Ambiguous POS) •“root” has multiple meanings (Ambiguous sense)
Word level ambiguity
Syntactic ambiguity
Anaphora resolution
Presupposition
“natural language processing” (Modification) •“A man saw a boy with a telescope.” (PP Attachment)
Word level ambiguity
Syntactic ambiguity
Anaphora resolution
Presupposition
“John persuaded Bill to buy a TV for himself.” (himself = John or Bill?)
Word level ambiguity
Syntactic ambiguity
Anaphora resolution
Presupposition
“He has quit smoking.” implies that he smoked before.
Word level ambiguity
Syntactic ambiguity
Anaphora resolution
Presupposition
An extensive lexical network for the English language
Wordnet
Synsets
Relationship
Words
Large collections of documents from various sources: news articles, research papers, books, digital libraries, e-mail messages, and Web pages, library database, etc.
Text databases
Information
Text analysis
Database
A field developed in parallel with database systems
Information retrieval
Text databases
Structured data
Data stored
The percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses)
Precision
Recall
Data
Text
The percentage of documents that are relevant to the query and were, in fact, retrieved
Recall
Precision
Text
Data
A document can be described by a set of representative keywords called
Index terms
Assignment
Attributes
Predicts that each document is either relevant or non-relevant based on the match of a document to the query
Boolean model
Query
A keyword T does not appear anywhere in the document, even though the document is closely related to T, e.g., data mining
Synonymy
Polysemy
Finds similar documents based on a set of common keywords
Similarity based retrieval
Text mining
Web mining
Set of words that are deemed “irrelevant”, even though they may appear frequently
Stop list
Stop word
Token
Several words are small syntactic variants of each other since they share a common word stem
Word stem
Term frequency
Stop list
Each entry frequent_table(i, j) = # of occurrences of the word ti in document di , sually, the ratio instead of the absolute number of occurrences is used
Term frequency table
Word stem
Stop word
Measure the closeness of a document to a query (a set of keywords)
Similarity metrics
Relative term
Similarity based
Associate a signature with each document
Signature file
Signature
Cluster documents by a common author
Similarity detection
Text mining
Web mining
Unusual correlation between entities
Link analysis
Anomaly detection
Sequence analysis
Predicting a recurring event
Sequence analysis
Link analysis
Anomaly detection
Ind information that violates usual patterns
Anomaly detection
Sequence analysis
Link analysis
Anchor text correlations with linked objects
Patterns in anchors/links
Patterns in text
Collect sets of keywords or terms that occur frequently together and then find the association or correlation relationships among them
Motivation
Assoociation
Preprocess the text data by parsing, stemming, removing stop words, etc.
Association
Analysis
Consider each document as a transaction
Evoke association mining algorithms
Term level association mining
No need for human effort in tagging documents
Term level association mining
Evoke association mining algorithm
Represent a doc by a term vector
Vector space model
Term vector model
E.g. “a”, “the”, “always”, “along”
Word stopping
Word stemming
E.g. “computer”, “computing”, “computerize” => “compute”
Word stemming
Word stopping
More frequent within a document more relevant to semantics
TF (Term frequency)
IDF(INverse document frequency)
Less frequent among documents more discriminative
TF
IDF
More frequent => more relevant to topic
Weighting
Normalization
Document length varies => relative frequency preferred
Normalization
Weighting
Is a collection of classification algorithms based on Bayes Theorem.
Naive bayes
Machine learning
Decision tree
A single independent variable is used to predict the value of a dependent variable.
Simple linear regression
Multiple linear regression
Two or more independent variables.
Multiple regression
Regression
Single regression
Measures the level of impurityin a group of examples
Impurity/entropy(informal)
Purity
Tells us how important a given attribute of the feature vectors is.
Information gain
Attribute gain
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