Index

A

• abstractive summarization
  • about / What is automatic text summarization?
• accuracy
  • about / Effectiveness – how accurate are the matches that we generate?
• adjacency list
  • about / Edge lists and adjacency lists
• adjacency list format
  • about / Adjacency list format
• adjacency matrix
  • about / Adjacency matrix
• Anaconda Python distribution
  • download link / How do we set up our data mining work environment?
• annotated corpus
  • about / Tagging parts of speech
• anomaly
  • about / What are data anomalies?
• antecedent
  • about / Association rules
• Apache
  • references / Apache Board meeting minutes
• Apache board meeting minutes
  • about / Apache Board meeting minutes
• apache_twitter
  • about / Locating missing data
• Apriori
  • about / Methods for finding frequent itemsets
• aspects
  • about / The structure of an opinion
• association rules
  • about / Towards association rules, Association rules
  • metrics / Towards association rules
  • support / Support
  • confidence / Confidence
  • data, example / An example with data
  • value - fixing flaw, adding / Added value – fixing a flaw in the plan
  • frequent itemsets, finding methods / Methods for finding frequent itemsets
  • discovering, in software project tags / A project – discovering association rules in software project tags
• atomic
  • about / Merging data
• attribute-based similarity matching
  • about / Attribute-based similarity matching
  • pairwise comparisons / Be careful of pairwise comparisons
  • rare values, leveraging / Leverage rare values
• attributes
  • about / The structure of an opinion
• attributes matching, methods
  • about / Methods for matching attributes
  • range-based, from target / Range-based or distance from target
  • distance, from target / Range-based or distance from target
  • string edit distance / String edit distance
  • Hamming distance / Hamming distance
  • Levenshtein distance / Levenshtein distance
  • Soundex / Soundex
• attributes of edges
  • about / What is a network?
• attributes of nodes
  • about / What is a network?
• automatic text summarization
  • about / What is automatic text summarization?
• automatic text summarization techniques
  • reference / What is automatic text summarization?

B

• bag of words
  • about / Important features of opinions
• bag of words (bow)
  • about / Gensim for topic modeling
• betweenness centrality
  • about / Betweenness centrality
• big data
  • about / What is data mining?
• blank data values
  • about / Missing data
• blocking methods
  • about / Efficiency – how long does it take to do the matching?
• bonus words
  • about / Sumy's Edmundson summarizer
• boundary errors
  • about / NER and partial matches
• box-and-whisker plot
  • about / Detecting outliers by combining statistics and visual mining
• boxplot
  • about / Detecting outliers by combining statistics and visual mining
  • reference link / Detecting outliers by combining statistics and visual mining
• Brown Corpus
  • about / Tagging parts of speech
  • reference link / Tagging parts of speech

C

• CamelCase
  • about / Why look for named entities?
• change detection problems
  • about / What are the techniques used in data mining?
• check constraint
  • about / Logic or semantic errors
• classification problems
  • about / What are the techniques used in data mining?
• closed path
  • about / Walks, paths, and trails in a network
• closeness centrality
  • about / Closeness centrality
• clustering-based outlier
  • reference link / Detecting outliers with machine learning
• clustering problems
  • about / What are the techniques used in data mining?
• code, entity matching project
  • about / The code
  • reference / The code
• code and text files, NER project
  • reference link / A simple NER tool
• coding
  • about / General-purpose data collections
• components
  • about / The structure of an opinion
• compound score
  • URL / Data analysis of chat messages
• confidence,association rules
  • about / Confidence
• consequent
  • about / Association rules
• context-based similarity matching
  • about / Context-based similarity matching
• corpus
  • about / Tagging parts of speech
• CREATE and INSERT statements
  • URL / Data analysis of e-mail messages
• CRISP-DM process
  • about / The CRISP-DM process
  • business understanding / The CRISP-DM process
  • data understanding / The CRISP-DM process
  • data preparation / The CRISP-DM process
  • modeling / The CRISP-DM process
  • evaluation / The CRISP-DM process
  • deployment / The CRISP-DM process
• cues
  • about / Sumy's Edmundson summarizer

D

• data
  • merging / Merging data
  • sets, merging vertically / Merging datasets vertically
  • sets, merging horizontally / Merging datasets horizontally
  • exploring / Exploring the data
• data, exploring
  • datasources table / Exploring the data
  • rf_developer_projects table / Exploring the data
• data, importing into graph structure
  • about / Importing data into a graph structure
  • adjacency list format / Adjacency list format
  • edge list format / Edge list format
  • GEXF format / GEXF and GraphML
  • GraphML format / GEXF and GraphML
  • graph data format (GDF) / GDF
  • Graph Data Format (GDF) / GDF
  • Python pickle / Python pickle
  • JavaScript Serialized Object Notation (JSON) / JSON
  • JSON node series / JSON node and link series
  • JSON link series / JSON node and link series
  • JSON trees / JSON trees
  • Pajek format / Pajek format
• data, social network
  • simple network metrics, generating / Generating simple network metrics
  • network parameters / Playing with the parameters of a network
  • subgraphs, analyzing / Analyzing subgraphs
  • cliques, analyzing / Analyzing cliques and centrality in the subgraphs
  • centrality in subgraphs, analyzing / Analyzing cliques and centrality in the subgraphs
  • change over time, finding / Looking for change over time
• data anomalies
  • about / What are data anomalies?
  • missing data / Missing data
  • missing data, fixing / Fixing missing data
  • data errors / Data errors
  • outliers / Outliers
• data append
  • about / Merging datasets vertically
• data errors
  • about / Data errors
  • truncated fields / Truncated fields
  • data type errors / Data type and character set errors
  • character set errors / Data type and character set errors
  • logic errors / Logic or semantic errors
  • semantic errors / Logic or semantic errors
• data file
  • URL / A project – discovering association rules in software project tags
• datafiles
  • reference link / Generating the network files
• data mining
  • about / What is data mining?
  • machine learning / What is data mining?
  • predictive analytics / What is data mining?
  • big data / What is data mining?
  • data science / What is data mining?
  • performing / How do we do data mining?
  • Fayyad et al. KDD process / The Fayyad et al. KDD process
  • Han et al. KDD process / The Han et al. KDD process
  • CRISP-DM process / The CRISP-DM process
  • Six Steps process / The Six Steps process
  • methodology / Which data mining methodology is the best?
  • techniques / What are the techniques used in data mining?, What techniques are we going to use in this book?
  • development environment, setting up / How do we set up our data mining work environment?
• data quality
  • about / Merging data
• data science
  • about / What is data mining?
• dataset, entity matching project
  • about / The dataset
• datasources table
  • datasource_id / Exploring the data
  • date_donated / Exploring the data
  • comments / Exploring the data
• data type errors
  • example / Data type and character set errors
• degree
  • about / Degree of a network
• degree centrality
  • about / Degree centrality
• dependency modeling problems
  • about / What are the techniques used in data mining?
• details
  • about / Locating missing data
• developer channel, Ubuntu
  • reference for text archive / Data preparation
• deviation detection problems
  • about / What are the techniques used in data mining?
• diameter
  • about / Diameter of a network
• directed network
  • about / What is a network?
• direction
  • about / What is a network?
• disjoint sets
  • leveraging / Leveraging disjoint sets
  • about / Leveraging disjoint sets
• distance
  • about / Diameter of a network
• Django IRC chat
  • about / Django IRC chat
  • reference link / Django IRC chat
• doc2bow()
  • about / Gensim for topic modeling
• document level
  • about / Document-level and sentence-level analysis
• domain
  • about / Frequent itemset mining basics
• domain knowledge
  • about / What is entity matching?
• doubletons
  • about / Frequent itemset mining basics

E

• edge list
  • about / Edge lists and adjacency lists
• edge list format
  • about / Edge list format
• edges
  • about / What is a network?
• entity
  • about / The structure of an opinion
• entity matching
  • about / What is entity matching?
  • data, merging / Merging data
  • techniques / Techniques for matching
  • attribute-based similarity matching / Attribute-based similarity matching
  • attributes matching, methods / Methods for matching attributes
  • disjoint sets, leveraging / Leveraging disjoint sets
  • context-based similarity matching / Context-based similarity matching
  • machine learning based entity matching / Machine learning-based entity matching
• entity matching project
  • about / Entity matching project
  • difficulties, with matching software projects / Difficulties with matching software projects
  • project names, matching / Matching on project names
  • people names, matching / Matching on people names
  • URLs, matching / Matching on URLs
  • topics and description keywords, matching / Matching on topics and description keywords
  • dataset / The dataset
  • code / The code
  • results / The results
• entity matching techniques
  • efficiency / Efficiency – how long does it take to do the matching?
  • effectiveness / Effectiveness – how accurate are the matches that we generate?
  • usefulness / Usefulness – how practical is the matching procedure to use?
• errors
  • about / What are data anomalies?
• explicit
  • about / The structure of an opinion
• extractive method
  • about / What is automatic text summarization?

F

• Facebook Research blog
  • download link / What is topic modeling?
• Fayyad et al. KDD process
  • data selection / The Fayyad et al. KDD process
  • data pre-processing / The Fayyad et al. KDD process
  • data transformation / The Fayyad et al. KDD process
  • data mining / The Fayyad et al. KDD process
  • data interpretation / The Fayyad et al. KDD process
  • data evaluation / The Fayyad et al. KDD process
• feature engineering
  • about / Sentiment analysis algorithms
• flaccid designator
  • about / Techniques for named entity recognition
• fliers
  • about / Detecting outliers by combining statistics and visual mining
  • reference link / Detecting outliers by combining statistics and visual mining
• FLOSSmole
  • URL / A project – discovering association rules in software project tags
  • reference link / GnuIRC summaries
• FLOSSmole.org
  • references / Exploring the data
• FLOSSmole data
  • about / The dataset
  • database tables / The dataset
• FLOSSmole project
  • URL / The dataset
• frequent itemsets
  • about / What are frequent itemsets?
  • diapers and beer urban legend example / The diapers and beer urban legend
  • mining basics / Frequent itemset mining basics

G

• gazetteer
  • about / Why look for named entities?
• GDF format
  • reference link / GDF
• general-purpose data collections
  • Hu and Liu's sentiment analysis lexicon / Hu and Liu's sentiment analysis lexicon
  • SentiWordNet / SentiWordNet
  • Vader sentiment / Vader sentiment
• generalizable
  • about / Usefulness – how practical is the matching procedure to use?
• general user channel, Ubuntu
  • reference for text archive / Data preparation
• Gensim
  • about / How do we set up our data mining work environment?
  • used, for text summarization / Text summarization using Gensim
  • used, for topic modeling / Gensim for topic modeling
• Gensim approach
  • reference / Text summarization using Gensim
• Gensim changelog
  • reference / Text summarization using Gensim
• Gensim documentation
  • reference link / Serializing a corpus
• Gensim LDA
  • download link / Latent Dirichlet Allocation
  • larger project / Gensim LDA for a larger project
• Gensim LDA model
  • applying, to documents / Applying a Gensim LDA model to new documents
• Gensim LDA objects
  • serializing / Serializing Gensim LDA objects
  • dictionary, serializing / Serializing a dictionary
  • corpus, serializing / Serializing a corpus
  • model, serializing / Serializing a model
• Gensim LDA passes
  • about / Understanding Gensim LDA passes
• Gensim LDA topics
  • about / Understanding Gensim LDA topics
  • example / Understanding Gensim LDA topics
• GEXF format
  • about / GEXF and GraphML
• glosses
  • about / SentiWordNet
• gnueIRCsummary.txt
  • reference link / GnuIRC summaries
• GnuIRC summaries
  • about / GnuIRC summaries
• graph
  • about / What is a network?
• graph data
  • representing / Representing graph data
• graph data, representing
  • adjacency matrix / Adjacency matrix
  • edge list / Edge lists and adjacency lists
  • adjacency list / Edge lists and adjacency lists
  • graph data structures, differences / Differences between graph data structures
  • data, importing into graph structure / Importing data into a graph structure
• graph data format (GDF)
  • about / GDF
• GraphML format
  • about / GEXF and GraphML
• graph trail
  • about / Walks, paths, and trails in a network
• graph walk
  • about / Walks, paths, and trails in a network
• Grubbs' test
  • about / Detecting outliers with modified z-scores
• gzipped
  • download link / The dataset

H

• Hamming distance
  • about / Hamming distance
• Han et al. KDD process
  • data cleaning / The Han et al. KDD process
  • data integration / The Han et al. KDD process
  • data selection / The Han et al. KDD process
  • data transformation / The Han et al. KDD process
  • data mining / The Han et al. KDD process
  • pattern evaluation / The Han et al. KDD process
  • knowledge representation / The Han et al. KDD process
• hapax
  • about / Important features of opinions
• horizontal merge
  • example / Merging datasets horizontally
• hot deck imputation
  • about / Use a similar value

I

• 2-itemsets
  • about / Frequent itemset mining basics
• 3-itemsets
  • about / Frequent itemset mining basics
• implicit
  • about / The structure of an opinion
• impute
  • about / Use a central measure
• in-degree
  • about / Degree of a network
• InterCaps
  • about / Why look for named entities?
• interestingness measures for association rules
  • about / Added value – fixing a flaw in the plan
• isolates
  • about / Components of a network

J

• JavaScript Serialized Object Notation (JSON)
  • about / JSON
• JSON link series
  • about / JSON node and link series
• JSON node series
  • about / JSON node and link series
• JSON trees
  • about / JSON trees

K

• knowledge discovery in databases (KDD)
  • about / What is data mining?
• knowledge discovery process
  • about / What is data mining?

L

• Last Observation Carried Forward (LOCF)
  • about / Use Last Observation Carried Forward
• Latent Dirichlet Allocation (LDA)
  • about / Latent Dirichlet Allocation
  • reference link / Latent Dirichlet Allocation
  • download link / Latent Dirichlet Allocation
• Latent Semantic Analysis (LSA)
  • reference / Sumy's LSA summarizer
• Levenshtein distance
  • about / Levenshtein distance
• lexicon
  • URL / Hu and Liu's sentiment analysis lexicon
• link analysis problems
  • about / What are the techniques used in data mining?
• links
  • about / What is a network?
• linusrants
  • about / Data analysis of e-mail messages
  • URL / Data analysis of e-mail messages
• Linux Kernel Mailing List (LKML)
  • about / Data analysis of e-mail messages
• LKML e-mails
  • about / LKML e-mails
• lkmlLinusAll.txt
  • reference link / Gensim LDA for a larger project
• logic errors
  • about / Logic or semantic errors

M

• machine learning
  • reference link / What is topic modeling?
  • outliers, detecting with / Detecting outliers with machine learning
• machine learning based entity matching
  • about / Machine learning-based entity matching
• manually, fixing
  • example / Fix the problem manually
• market basket analysis
  • about / What are frequent itemsets?
  • market / Frequent itemset mining basics
  • basket / Frequent itemset mining basics
  • items / Frequent itemset mining basics
• Matrix Market (MM) format
  • about / Serializing a corpus
  • reference link / Serializing a corpus
• maximum normalized residual test
  • about / Detecting outliers with modified z-scores
• Message Understanding Conference (MUC)
  • about / Handling partial matches
• minimum support threshold
  • about / Support
• missing data
  • about / Missing data
  • locating / Locating missing data
  • zero values / Zero values
• missing data, fixing
  • about / Fixing missing data
  • rows, ignoring / Ignore the problem rows
  • manually, fixing / Fix the problem manually
  • fabricated value used / Use a fabricated value
  • central measure used / Use a central measure
  • Last Observation Carried Forward (LOCF) used / Use Last Observation Carried Forward
  • similar value used / Use a similar value
  • most likely value used / Use the most likely value
• modified z-score
  • about / Detecting outliers with modified z-scores
• modified z-scores
  • outliers, detecting with / Detecting outliers with modified z-scores
• multi-document
  • about / What is automatic text summarization?
• multiple components
  • about / Components of a network
• multivariate data sets
  • about / Statistical detection of outliers
• MySQL
  • URL / How do we set up our data mining work environment?

N

• named entity recognition (NER)
  • about / Why look for named entities?
  • techniques / Techniques for named entity recognition
  • part of speech (POS), tagging / Tagging parts of speech
• named entity recognition (NER) project
  • about / Named entity recognition project
  • NER tool / A simple NER tool
• named entity recognition (NER) systems
  • building / Building and evaluating NER systems
  • evaluating / Building and evaluating NER systems
  • partial matches / NER and partial matches
  • partial matches handling / Handling partial matches
• named entity recognition (NER) tool
  • about / A simple NER tool
  • Apache board meeting minutes / Apache Board meeting minutes
  • Django IRC chat / Django IRC chat
  • GnuIRC summaries / GnuIRC summaries
  • LKML e-mails / LKML e-mails
• natural language processing (NLP)
  • about / The basics of sentiment analysis
• Natural Language Toolkit (NLTK)
  • about / How do we set up our data mining work environment?
• negation words
  • about / Important features of opinions
• network
  • about / What is a network?
  • measuring / Measuring a network
• network, measuring
  • degree / Degree of a network
  • diameter / Diameter of a network
  • graph walk / Walks, paths, and trails in a network
  • graph trail / Walks, paths, and trails in a network
  • path / Walks, paths, and trails in a network
  • components / Components of a network
  • centrality / Closeness centrality
  • degree centrality / Degree centrality
  • betweenness centrality / Betweenness centrality
  • centrality, measures / Other measures of centrality
• NetworkX
  • installing / Understanding our data as a network
• NetworkX file formats
  • reference link / Pajek format
• neutral word
  • about / SentiWordNet
• NLTK
  • used, for naive text summarization / Naive text summarization using NLTK
• NLTK documentation page
  • URL / Data analysis of e-mail messages
• nodes
  • about / What is a network?
• novelty
  • about / Outliers
• nullable
  • about / Locating missing data
• null data values
  • about / Missing data
• null words
  • about / Sumy's Edmundson summarizer

O

• objectivity score
  • about / SentiWordNet
• opinion mining
  • about / What is sentiment analysis?
  • reference / What is sentiment analysis?
• opinion shifters
  • about / Important features of opinions
• opinion words
  • about / Important features of opinions
• out-degree
  • about / Degree of a network
• outlier
  • about / What are data anomalies?, Outliers
• outlier detection
  • reference link / Detecting outliers with machine learning
• outliers
  • visual mining / Visual mining for outliers
  • statistical detection / Statistical detection of outliers
• outliers, statistical detection
  • outliers, detecting with modified z-scores / Detecting outliers with modified z-scores
  • outliers, detecting by combining statistics / Detecting outliers by combining statistics and visual mining
  • outliers, detecting by combining visual mining / Detecting outliers by combining statistics and visual mining
  • outliers, detecting with machine learning / Detecting outliers with machine learning
• overfitting
  • about / Sentiment analysis algorithms

P

• Pajek format
  • about / Pajek format
• partial matches
  • about / NER and partial matches
  • strict scoring / NER and partial matches
  • lenient scoring / NER and partial matches
  • partial scoring / NER and partial matches
• part of speech (POS)
  • about / Tagging parts of speech
  • tagging / Tagging parts of speech
  • named entities, classes / Classes of named entities
• part of speech, abbreviations
  • reference link / Tagging parts of speech
• parts of speech
  • about / Important features of opinions
• path
  • about / Walks, paths, and trails in a network
• pendant nodes
  • about / Playing with the parameters of a network
• Penn, noun abbreviations
  • example / Tagging parts of speech
• Penn Treebank tagger
  • about / Tagging parts of speech
• position of word
  • about / Important features of opinions
• POS tagger
  • about / Tagging parts of speech
• precision
  • about / Effectiveness – how accurate are the matches that we generate?
• profile
  • about / Leveraging disjoint sets
• Python pickle
  • about / Python pickle

Q

• question answering (QA) systems
  • about / Why look for named entities?

R

• real-world project, network
  • about / A real project
  • data, exploring / Exploring the data
  • network files, generating / Generating the network files
  • data, social network / Understanding our data as a network
• recall
  • about / Effectiveness – how accurate are the matches that we generate?
• regression problems
  • about / What are the techniques used in data mining?
• relational database management systems (RDBMS)
  • about / Locating missing data
• results, entity matching project
  • about / The results, How many entity matches did we find?
  • entity matches / How many entity matches did we find?
  • pairs, identifying / How good are the pairs we found?
• rf_developer_projects table
  • datasource_id / Exploring the data
  • dev_loginname / Exploring the data
  • proj_unixname / Exploring the data
• rigid designator
  • about / Techniques for named entity recognition
• Rmagick on RubyForge
  • about / Two examples
  • references / Two examples
• Rmagick on RubyGems
  • about / Two examples
  • references / Two examples
• RubyForge
  • URL / Matching on URLs
• Ruby on Rails
  • URL / How many entity matches did we find?

S

• Scikit-learn tutorial
  • URL / How do we set up our data mining work environment?
• semantic errors
  • about / Logic or semantic errors
  • example / Logic or semantic errors
• sentiment analysis
  • about / What is sentiment analysis?
  • reference / What is sentiment analysis?
  • algorithms / Sentiment analysis algorithms
  • general-purpose data collections / General-purpose data collections
• sentiment analysis, basics
  • about / The basics of sentiment analysis
  • opinion, structure / The structure of an opinion
  • document-level analysis / Document-level and sentence-level analysis
  • sentence-level analysis / Document-level and sentence-level analysis
  • opinions, features / Important features of opinions
• sentiment intensity
  • about / Vader sentiment
• sentiment mining application
  • about / Sentiment mining application
  • project, motivating / Motivating the project
  • data preparation / Data preparation
  • chat messages, data analysis / Data analysis of chat messages
  • e-mail messages, data analysis / Data analysis of e-mail messages
• sentiment score
  • URL / Data analysis of chat messages
• sentiment words
  • about / Important features of opinions
• SentiWordNet
  • URL / SentiWordNet
• sequence analysis problems
  • about / What are the techniques used in data mining?
• set notation
  • about / Frequent itemset mining basics
• significant words
  • about / What is automatic text summarization?
• simpleTextSummaryNLTK.py
  • reference / Naive text summarization using NLTK
• single-document
  • about / What is automatic text summarization?
• Six Steps process
  • problem statement / The Six Steps process
  • data collection / The Six Steps process
  • data storage / The Six Steps process
  • data cleaning / The Six Steps process
  • data mining / The Six Steps process
  • representation / The Six Steps process
  • visualization / The Six Steps process
  • problem resolution / The Six Steps process
• software project tags
  • association rules, discovering / A project – discovering association rules in software project tags
• Soundex
  • about / Soundex
• source lines of code (SLOC)
  • about / Outliers
• specificity
  • about / Effectiveness – how accurate are the matches that we generate?
• stigma words
  • about / Sumy's Edmundson summarizer
• stopwords
  • about / Naive text summarization using NLTK
• string edit distance
  • about / String edit distance
• subgraphs
  • reference link / Analyzing subgraphs
• subjectivity classification
  • about / Document-level and sentence-level analysis
• summarization problems
  • about / What are the techniques used in data mining?
• SUMMRY
  • about / Tools for text summarization
  • reference / Tools for text summarization
• Sumy
  • used, for text summarization / Text summarization using Sumy
  • references / Text summarization using Sumy
• Sumy's Edmundson summarizer
  • reference / Sumy's Edmundson summarizer
  • about / Sumy's Edmundson summarizer
• Sumy's LSA summarizer
  • about / Sumy's LSA summarizer
• Sumy's Luhn summarizer
  • about / Sumy's Luhn summarizer
• Sumy's TextRank summarizer
  • about / Sumy's TextRank summarizer
• sustainable
  • about / Usefulness – how practical is the matching procedure to use?

T

• target
  • about / The structure of an opinion
• target data
  • about / The Fayyad et al. KDD process
• terms
  • about / Important features of opinions
• text samples
  • download link / Gensim for topic modeling
• text summarization
  • tools / Tools for text summarization
  • naive text summarization, NLTK used / Naive text summarization using NLTK
  • using Gensim / Text summarization using Gensim
  • Sumy used / Text summarization using Sumy
• text summarization, methods
  • Sumy's Luhn summarizer / Sumy's Luhn summarizer
  • Sumy's TextRank summarizer / Sumy's TextRank summarizer
  • Sumy's LSA summarizer / Sumy's LSA summarizer
  • Sumy's Edmundson summarizer / Sumy's Edmundson summarizer
• topic modeling
  • about / What is topic modeling?
  • Gensim used / Gensim for topic modeling
  • Gensim LDA topics / Understanding Gensim LDA topics
  • Gensim LDA passes / Understanding Gensim LDA passes
  • Gensim LDA model, applying to documents / Applying a Gensim LDA model to new documents
  • Gensim LDA objects, serializing / Serializing Gensim LDA objects
• training examples
  • about / Sentiment analysis algorithms
• tree structure
  • about / JSON trees
• tripletons
  • about / Frequent itemset mining basics
• true positives (TP)
  • about / How good are the pairs we found?
• type errors
  • about / Data type and character set errors

U

• Ubuntu
  • URL / Data preparation
• undirected network
  • about / What is a network?
• univariate data sets
  • about / Statistical detection of outliers
• unsupervised
  • about / What is topic modeling?
• upward closure property
  • about / Methods for finding frequent itemsets

V

• Vader sentiment
  • URL / Vader sentiment
  • URL, for specific lexicon / Vader sentiment
• Vapor on RubyForge
  • about / Two examples
  • references / Two examples
• Vapor on RubyGems
  • about / Two examples
  • references / Two examples
• vertical merge
  • example / Merging datasets vertically
• vertices
  • about / What is a network?
• visual mining
  • about / Visual mining for outliers

W

• weighted network
  • about / What is a network?

Z

• z-score
  • about / Detecting outliers with modified z-scores
  • reference link / Detecting outliers with modified z-scores