Understanding Data Mining in Business Intelligence
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Questions and Answers

What is Data Mining?

  • A process that focuses on visualizing data rather than extracting information from databases.
  • A process that relies solely on manual extraction of information from large databases.
  • A process that uses statistical, mathematical, artificial intelligence and machine learning techniques to extract useful information from large databases. (correct)
  • A process that only involves basic data manipulation techniques to extract information from small databases.

Where is Data Mining in Business Intelligence?

  • Data mining is a part of business intelligence. (correct)
  • Data mining is the sole component of business intelligence.
  • Data mining is completely separate from business intelligence.
  • Data mining is not relevant to business intelligence.

Why do we need Data Mining in managerial decision making?

  • To perform basic data visualization for managerial decision making.
  • To manually extract information from databases for managerial decision making.
  • To perform statistical and mathematical analysis, hypothesis testing, prediction, and customer scoring models. (correct)
  • To synchronize data in databases for managerial decision making.

What are the major characteristics of Data Mining?

<p>Data are often buried deep within very large databases and sophisticated tools are used to clean and synchronize data for the best result. (B)</p> Signup and view all the answers

What is the main focus of customer analytics in CRM and revenue management?

<p>Clustering customers into groupings based on better understanding (C)</p> Signup and view all the answers

What is the primary goal of text mining?

<p>To extract additional useful information from stored data (C)</p> Signup and view all the answers

What does web content mining primarily involve?

<p>Extraction of useful information from Webpages (C)</p> Signup and view all the answers

What is the purpose of calculating weights of terms in text mining?

<p>$1) Term frequency factor (the actual number of times the word appears in a document) and 2) Inverse document frequency (the number of times the word appears in all document in a set)$ (D)</p> Signup and view all the answers

How does web usage mining contribute to understanding user behavior?

<p>$Generated through web page visits, transactions and web server logs$ (A)</p> Signup and view all the answers

What is an example application of data mining to non-structured or less structured text files?

<p>Applying data mining to customer database to discover patterns. (D)</p> Signup and view all the answers

What does optimization involve in customer analytics?

<p>Detecting patterns to optimize transaction and customer interaction. (D)</p> Signup and view all the answers

What type of mining focuses on understanding user behavior through web page visits and transactions?

<p>Web usage mining. (B)</p> Signup and view all the answers

What is the primary focus of web structure mining?

<p>Generating information from the links included in WebPages. (B)</p> Signup and view all the answers

What does text mining primarily focus on?

<p>Application of data mining to nonstructured or less structured text files. (C)</p> Signup and view all the answers

What are the two main types of data mining?

<p>Hypothesis-driven and discovery-driven (C)</p> Signup and view all the answers

What is the major difference between cluster analysis and classification?

<p>Classification sorts cases into groups, while cluster analysis identifies common characteristics shared by group members. (B)</p> Signup and view all the answers

What does association data mining establish relationships between?

<p>Items that occur together (B)</p> Signup and view all the answers

In which business sector is data mining used to understand customer behavior?

<p>Retailing (A)</p> Signup and view all the answers

What do prediction methods use to predict future values?

<p>Variables (C)</p> Signup and view all the answers

What is the aim of data clustering algorithms?

<p>To create groups with maximum similarity and minimum similarity across groups (A)</p> Signup and view all the answers

What is the primary purpose of sequence discovery in data mining?

<p>Identifying associations over time, tracking elapsed time between events and frequency of occurrences (A)</p> Signup and view all the answers

Which type of induction method is classification?

<p>Supervised (D)</p> Signup and view all the answers

What does retail-focused data mining primarily involve analyzing?

<p>Demographic data, transaction data, and online interaction data (C)</p> Signup and view all the answers

In which sector is association rule discovery often used?

<p>Retailing for market basket analysis (B)</p> Signup and view all the answers

What are the two types of data mining based on their approach?

<p>Hypothesis-driven and discovery-driven (B)</p> Signup and view all the answers

  1. What is the primary purpose of text mining?

<p>The primary purpose of text mining is to extract meaningful numerical indices from unstructured or less structured text files and then process these indices using various data mining algorithms.</p> Signup and view all the answers

  1. What are the steps involved in mining text?

<p>The steps involved in mining text include eliminating commonly used words (stop-words), replacing words with their stems or roots, considering synonyms and phrases, and calculating the weights of the remaining terms based on their frequency and importance.</p> Signup and view all the answers

  1. How does text mining contribute to competitive advantage for organizations?

<p>Text mining allows organizations to visualize relationships between documents, such as policies, memos, emails, and minutes of meetings, which can be a major source of competitive advantage.</p> Signup and view all the answers

  1. What is an example application of text mining in the airline industry?

<p>An example application of text mining in the airline industry is to focus on key problem areas through pattern identification by accessing incident reports to increase the quality of service, such as identifying incidents that might lead to trouble and helping management stop the issue.</p> Signup and view all the answers

  1. How does web usage mining contribute to understanding user behavior?

<p>Web usage mining contributes to understanding user behavior by generating insights through web page visits, transactions, and web server logs, which are useful for customer relationship management (CRM) and web analytics.</p> Signup and view all the answers

Text mining primarily focuses on extracting meaningful numerical indices from ______ text files

<p>unstructured or less structured</p> Signup and view all the answers

Web mining is the discovery through the analysis of interesting and useful information from the ______, about the web and usually using a web based tool

<p>web</p> Signup and view all the answers

Web content mining involves the extraction of useful information from ______

<p>Webpages</p> Signup and view all the answers

Web structure mining generates information from the ______ included in WebPages

<p>links</p> Signup and view all the answers

Web usage mining is generated through web page visits, transactions and web server logs, and is useful for ______ and understanding user behavior

<p>CRM</p> Signup and view all the answers

What is the primary purpose of a data warehouse in Business Intelligence?

<p>To support decision-making applications by providing access to prepared data (B)</p> Signup and view all the answers

What is the main function of business analytics in Business Intelligence?

<p>To allow users to create reports, queries, and conduct data analysis (D)</p> Signup and view all the answers

What is the purpose of data mining in Business Intelligence?

<p>To look for hidden patterns in data for predicting future behavior (B)</p> Signup and view all the answers

What is the role of OLAP (Online Analytical Processing) in Business Intelligence?

<p>To enable users to interactively analyze multidimensional data (B)</p> Signup and view all the answers

What is the primary objective of Business Performance Management (BPM) based on balanced scorecard methodology (BSC)?

<p>To optimize overall performance of an organization (C)</p> Signup and view all the answers

Which type of users in Business Intelligence (BI) are typically met by tools such as dashboards?

<p>Executives - top managers of any organization (A)</p> Signup and view all the answers

What is the major theory of Business Intelligence (BI) that distinguishes it from transaction processing?

<p>BI is not transaction processing, hence OLTP vs. OLAP (D)</p> Signup and view all the answers

What is the primary focus of Online Transaction Processing Systems (OLTP)?

<p>Handling a company’s routine ongoing business activities (B)</p> Signup and view all the answers

What distinguishes Online Analytic Processing (OLAP) from Online Transaction Processing Systems (OLTP)?

<p>Enabling user queries and analysis of historical data (C)</p> Signup and view all the answers

What are the three major types of BI users according to the provided text?

<p>IT staff, Power users, Executives - top managers of any organization. (A)</p> Signup and view all the answers

Flashcards

Data Mining

Using statistical, mathematical, AI, and machine learning to find useful info in large databases

Data Mining in BI

Part of Business Intelligence, used to analyze data

Data Mining in Decision Making

Helps managers make decisions using statistical analysis, predictions

Data Mining Characteristics

Data often buried, requires complex cleaning and synchronizing

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Customer Analytics in CRM

Grouping customers for better understanding and service

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Text Mining Purpose

Extracting useful info from unstructured text documents

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Web Content Mining

Extracting info from web pages

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Term Weights in Text Mining

Based on Term Frequency and Inverse Document Frequency

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Web Usage Mining

Understanding user behavior by analyzing web data

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Data Mining in Text Files

Applying data mining to non-structured or less structured text files

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Optimization in Customer Analytics

Using data patterns to improve transactions and interactions

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Cluster Analysis vs. Classification

Classification groups cases; cluster analysis groups with similar characteristics

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Association Data Mining

Finding relationships between items that frequently occur together

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Prediction Methods

Using variables to predict future values

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Data Clustering

Creating groups with maximum internal similarity, minimum between-group similarity

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Sequence Discovery

Identifying associations over time and frequency of occurrences

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Classification Induction

A supervised learning method of sorting cases into groups

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Retail Data Mining

Analyze demographic, transaction, and online interaction data from customers

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Association Rule Discovery in Retail

Finding relationships between products purchased together

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Hypothesis-Driven vs. Discovery-Driven Data Mining

Hypothesis-driven starts with a theory; discovery-driven seeks patterns

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Text Mining Focus

Extracting numerical indices from unstructured text

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Web Mining

Discovering beneficial insights from the web

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Web Structure Mining

Generating information from web page links

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Data Warehouse Purpose in BI

Supports decision-making by providing access to prepared data

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Business Analytics Function

Enables users to create reports, queries, and data analysis

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Data Mining Purpose in BI

Finding hidden patterns in data for predicting future behavior

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OLAP in Business Intelligence

Enables interactive analysis of multidimensional data

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Business Performance Management (BPM)

Optimizing organizational performance using balanced scorecards

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BI Dashboard Users

Executives (top managers) are the main users of BI dashboards

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OLTP vs. OLAP

OLTP handles day-to-day transactions; OLAP analyzes historical data for insights

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Online Transaction Processing (OLTP)

Handles daily business activities

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Online Analytical Processing (OLAP)

Analyzes historical data interactively

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Study Notes

Data Mining Methods and Applications

  • Data mining activities can lead to unexpected results, requiring creative decision-making.
  • Prediction methods use variables to predict future values, while descriptive methods find human-interpretable patterns.
  • Data mining tasks fall into four categories: classification, clustering, association rule discovery, and sequential pattern discovery.
  • Classification, a supervised induction method, is commonly used for analyzing historical data and predicting future behavior.
  • Data clustering algorithms partition a database into segments with similar qualities, aiming to create groups with maximum similarity and minimum similarity across groups.
  • The major difference between cluster analysis and classification is that classification sorts cases into groups, while cluster analysis identifies common characteristics shared by group members.
  • Association data mining establishes relationships between items that occur together, often used in market basket analysis.
  • Sequence discovery identifies associations over time, tracking elapsed time between events and frequency of occurrences.
  • Two types of data mining are hypothesis-driven, starting with a proposition, and discovery-driven, uncovering unknown facts.
  • Data mining is used in various business sectors including banking, retailing, manufacturing, marketing, government, health, airlines, and broadcasting.
  • Understanding customer behavior in retail involves analyzing demographic data, transaction data, and online interaction data.
  • In retail, data mining focuses on web analytics, gathering web statistics to track customer behavior and adjust websites to meet customer needs.

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Related Documents

Data mining.pptx
Introduction to BI (1).ppt

Description

This quiz explores the concept of data mining and its applications in business intelligence. It covers the use of statistical, mathematical, and machine learning techniques to extract valuable information from large databases.

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