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Questions and Answers
Which of the following words appears most frequently in the document?
Which of the following words appears most frequently in the document?
Which of the following words is NOT found in the document?
Which of the following words is NOT found in the document?
Which of the following words is used to describe the quality of a product?
Which of the following words is used to describe the quality of a product?
Which of the following words best describes the tone of the document?
Which of the following words best describes the tone of the document?
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Which pair of words are most commonly used together in the document?
Which pair of words are most commonly used together in the document?
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Based on the document, which of the following is likely a type of food establishment?
Based on the document, which of the following is likely a type of food establishment?
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Which of the following words is used to describe the quantity of something?
Which of the following words is used to describe the quantity of something?
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Which of the following words is NOT related to the concept of time?
Which of the following words is NOT related to the concept of time?
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Which word has the highest count in the reviews?
Which word has the highest count in the reviews?
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What type of food has a document frequency unique to its restaurant based on the content?
What type of food has a document frequency unique to its restaurant based on the content?
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Which of the following words appears with the most frequency in the context of food reviews?
Which of the following words appears with the most frequency in the context of food reviews?
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Which term relates to a specific characteristic of the restaurant instead of general dining?
Which term relates to a specific characteristic of the restaurant instead of general dining?
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Which word represents a common student misconception and appears frequently in reviews?
Which word represents a common student misconception and appears frequently in reviews?
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What concept in the reviews suggests the importance of words unique to a particular restaurant?
What concept in the reviews suggests the importance of words unique to a particular restaurant?
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Which food-related word has a relatively lower usage frequency in the reviews?
Which food-related word has a relatively lower usage frequency in the reviews?
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What word would not be considered unique to any specific review based on document frequency?
What word would not be considered unique to any specific review based on document frequency?
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What is the primary purpose of data mining?
What is the primary purpose of data mining?
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Which of the following is NOT a type of model used in data mining?
Which of the following is NOT a type of model used in data mining?
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What technique might be used during the preprocessing stage of data mining to improve data quality?
What technique might be used during the preprocessing stage of data mining to improve data quality?
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During the data analysis pipeline, what comes after the data mining step?
During the data analysis pipeline, what comes after the data mining step?
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Which of the following describes the term 'dimensionality reduction' in data preprocessing?
Which of the following describes the term 'dimensionality reduction' in data preprocessing?
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In which context is behavioral data typically used in data mining?
In which context is behavioral data typically used in data mining?
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What aspect of data mining involves turning the results into actionable insights for users?
What aspect of data mining involves turning the results into actionable insights for users?
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What is one major challenge in data mining due to the variety of data sources?
What is one major challenge in data mining due to the variety of data sources?
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What was the author's opinion on the fried portabella-thingy?
What was the author's opinion on the fried portabella-thingy?
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Which aspect of the Shake Shack experience did the author highlight?
Which aspect of the Shake Shack experience did the author highlight?
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How did the author describe the shake from Shake Shack?
How did the author describe the shake from Shake Shack?
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What flavor did the author mention was added to the vanilla shake?
What flavor did the author mention was added to the vanilla shake?
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What did the author think about comparing Shake Shack to In-N-Out or Five Guys?
What did the author think about comparing Shake Shack to In-N-Out or Five Guys?
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What was the author's overall impression of the food at Shake Shack?
What was the author's overall impression of the food at Shake Shack?
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Which word best describes the author's feeling while eating at Shake Shack?
Which word best describes the author's feeling while eating at Shake Shack?
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What type of dining environment did Shake Shack offer, according to the author?
What type of dining environment did Shake Shack offer, according to the author?
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Which food item did the author specifically mention as a part of their meal?
Which food item did the author specifically mention as a part of their meal?
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What did the author feel might have contributed to their 'food coma' experience?
What did the author feel might have contributed to their 'food coma' experience?
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Which word did the author use to describe the experience of dining at Shake Shack?
Which word did the author use to describe the experience of dining at Shake Shack?
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What was noted as a conflict with the meal by the author?
What was noted as a conflict with the meal by the author?
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What kind of shake did the author enjoy at Shake Shack?
What kind of shake did the author enjoy at Shake Shack?
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Which of the following describes the author's view about the price of food at Shake Shack?
Which of the following describes the author's view about the price of food at Shake Shack?
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Which of the following is NOT an example of a data quality problem?
Which of the following is NOT an example of a data quality problem?
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What is the primary motivation for using sampling in data mining?
What is the primary motivation for using sampling in data mining?
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What defines a representative sample?
What defines a representative sample?
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What might happen if a sample introduces bias?
What might happen if a sample introduces bias?
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What challenge can arise when calculating the average height of people in Ioannina using the entire population?
What challenge can arise when calculating the average height of people in Ioannina using the entire population?
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Which of the following best exemplifies a situation where sampling would be beneficial?
Which of the following best exemplifies a situation where sampling would be beneficial?
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What type of data issue is represented by entries with NULL values?
What type of data issue is represented by entries with NULL values?
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Why is it often necessary to consider data quality before conducting analysis?
Why is it often necessary to consider data quality before conducting analysis?
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What does TF(w,d) represent in the context of document analysis?
What does TF(w,d) represent in the context of document analysis?
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How is IDF(w) calculated?
How is IDF(w) calculated?
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What is the combined calculation of TF-IDF(w,d)?
What is the combined calculation of TF-IDF(w,d)?
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Which of the following statements is true regarding stop words in TF-IDF?
Which of the following statements is true regarding stop words in TF-IDF?
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In document analysis, which word is likely to have the highest IDF value?
In document analysis, which word is likely to have the highest IDF value?
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What does a high TF-IDF value for a word indicate?
What does a high TF-IDF value for a word indicate?
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Which of the following is NOT a use of TF-IDF?
Which of the following is NOT a use of TF-IDF?
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Which option represents a potential decision to make when dealing with real data?
Which option represents a potential decision to make when dealing with real data?
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In a list ordered by TF-IDF, which word would likely appear at the top?
In a list ordered by TF-IDF, which word would likely appear at the top?
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How does the presence of many documents impact the IDF of a word?
How does the presence of many documents impact the IDF of a word?
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If a word has a TF of 0, what is its TF-IDF score?
If a word has a TF of 0, what is its TF-IDF score?
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What role do stop words play in TF-IDF calculations?
What role do stop words play in TF-IDF calculations?
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Which of the following terms is an example of TF in a sentence?
Which of the following terms is an example of TF in a sentence?
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What property of TF-IDF helps in distinguishing important words in texts?
What property of TF-IDF helps in distinguishing important words in texts?
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Study Notes
Data Mining Lecture 2
- Data mining is the use of efficient techniques to analyze very large datasets and extract useful, possibly unexpected patterns.
- Data mining is the analysis of large observational datasets to find previously unknown relationships and summarize data in ways that are understandable and useful to analysts.
- Data mining is the discovery of models for data.
- Data mining models can explain data (e.g., a single function), predict future instances, summarize data, or extract prominent features.
Why Data Mining is Needed
- Today, there are massive amounts of complex data generated from multiple, interconnected sources.
- Examples of data types include scientific data from different disciplines (weather, astronomy, biology), huge text collections (web, news, tweets), transaction data (retail, credit cards), behavioral data (mobile phone logs, browsing history), and networked data (web, social networks).
- Combining these various data types in different ways is commonplace.
- Analyzing this data to extract knowledge is crucial for both commercial and scientific purposes.
- Data mining solutions need to scale to handle the vast amounts of data.
The Data Analysis Pipeline
- Data mining is not the only step in the analysis process.
- Data preprocessing is a crucial step for data cleaning (handling noisy, incomplete, and inconsistent data), sampling, dimensionality reduction, and feature selection.
- Pre- and post-processing are important parts of data mining.
- Post-processing involves making the data actionable and useful by creating statistical analysis and visualizations.
Data Quality Problems
- Examples of data quality issues include noise (outliers), missing values, and duplicate data.
- Inconsistent duplicate entries and missing values might mean mistakes in data entry
Sampling
- Sampling is a critical technique in data selection, used for both preliminary investigation and final analysis.
- Statistical sampling is done since complete data analysis is too expensive or time-consuming.
- Example: calculating the average height of all Ioannina residents by sampling instead of measuring everyone.
- Example: determining the fraction of documents with at least 100 common words in a million-document dataset.
- Example: calculating the percentage of tweets that use "Greece" in daily tweets (300M).
Types of Sampling
-
Simple random sampling: Each item has an equal chance of being selected.
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Sampling without replacement: Each selected item is removed from the population.
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Sampling with replacement: Items can be selected more than once. This helps with analytical computations.
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Stratified sampling: Divides the dataset into subgroups (strata) and then randomly samples from each subgroup to ensure representation.
-
Example: sampling for credit card transactions to differentiate between fraudulent and legitimate transactions by selecting and grouping fraudulent and legitimate transactions and then sampling from each category.
Sample Size
- The necessary sample size depends on the complexity of the data and the desired accuracy of the resulting analysis.
- Choosing a proper sample size is essential for accurate analysis.
A Data Mining Challenge
- How to sample one integer uniformly from a stream (unknown size) using only a constant amount of memory.
- Reservoir sampling is a solution to this problem.
A (Detailed) Preprocessing Example
- Mining Yelp and Foursquare reviews involves data collection, preprocessing, data mining, and post-processing.
- Collect comments/reviews, clean and organize the data.
Data Collection
- Use public APIs to collect data (Facebook, Twitter, Wikipedia, etc.).
- Use customized crawlers to extract data.
- Clean and process the collected data to extract relevant parts and avoid crawling etiquette issues.
Mining Task
- Collect all reviews for the top 10 most reviewed restaurants in a specific area on Yelp.
- Find terms that best describe the restaurants.
First Cut
- Remove punctuation and convert to lowercase, with proper white space.
- Keep the most popular words.
Second Cut
- Remove commonly used stop words ("a," "the," etc.).
- Stop-word lists are readily available online.
Third Cut
- Use TF-IDF to calculate the importance of words. TF-IDF, or term frequency-inverse document frequency, is important to remove common words to find the truly unique information.
- TF-IDF automatically handles stop-words.
Decisions, Decisions...
- When mining real data, there are various choices and decisions.
- Decide what data to collect, how much, and how long to collect it.
- Removing less useful data.
- Consider how to weigh different pieces of data.
- Practical, application-dependent decisions are essential.
Exploratory Data Analysis
- Explore data to understand its properties.
- Summarize data using frequency, location (mean), and spread (standard deviation).
- Calculate summary statistics.
Frequency and Mode
- Frequency is the percentage of occurrence of an attribute value in a dataset.
- Mode is the most frequent attribute value, commonly used with categorical data.
Percentiles
- Useful for continuous data, identifying values at specific percentile thresholds.
Measures of Location
- Mean, median, and trimmed mean, frequently used measures of location. Important to consider that the mean can be affected by extreme values (outliers).
Measures of Spread
- Range, variance, and standard deviation, commonly used measures of data spread.
Normal Distribution
- Important distribution in probabilities and statistics.
- Characterized by its mean and standard deviation.
Not Everything is Normally Distributed
- Data distribution may not always follow a normal pattern.
Power-Law Distribution
- Data distribution may fit a power law, with a linear relationship in log-log space.
Zipf's Law
- Power laws are frequently observed.
- Linear relationship between word rank and frequency are characteristic of power laws.
Power Laws Are Everywhere
- Power laws are observed in various data, including web links, friendships, word occurrences, income distribution.
The Long Tail
- Concept describing popularity of product types that may not be as popular as others, but that are still in market demand.
Post-processing
- Visualization is essential for discovering patterns in data, such as histograms, plots. Human eyes can easily spot patterns in visualized data
Additional Notes
- Data quality is important for reliable data mining results.
- Techniques like sampling and data visualization can help to better understanding data distribution patterns.
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Description
Explore the essentials of data mining in this quiz. Understand how data mining techniques help analyze large datasets and uncover useful patterns. Learn about the importance of data mining in handling complex data from various sources.
