Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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Presently‚ Business Intelligence (BI) analysis solutions are manually operated which makes it time consuming and difficult for users to extract useful information from a multidimensional set of data. Henceforth‚ by applying Data Mining (DM) algorithms for Business Intelligence‚ it is possible to automate the analysis process‚ thus comes the ability to extract patterns and other important information from the data set. Understanding the reason why Data Mining is needed in Business Intelligence and also
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difference between Data‚ Information and Knowledge? Data‚ information and knowledge are often referred to and used to represent the same thing. However‚ each term has its own meaning. By defining what data‚ information and knowledge mean individually‚ a greater understanding can be reached. It is also important to look at how they interact with each other. Knowledge‚ by definition‚ is the theoretical or practical understanding of a subject. It is the acquisition of information through education
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Knowledge and Information � PAGE �2� Running Head: KNOWLEDGE AND INFORMATION Knowledge and Information [The Writer ’s Name] [The Name of the Institution]� Knowledge and Information Introduction According to Webb (1998‚ 110-15)‚ information is the potential for knowledge. Knowledge is actionable information that can be used across the entire organisation. The more knowledge an organisation possesses‚ the more it can reduce uncertainties. Davenport and Prusak (1997‚ 99-102)‚ refers knowledge
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DATA | INFORMATION | 123424331911 | Your winning lottery ticket number | 140593 | Your date of birth | Aaabbbccd | The grades you want in your GCSEs | Data and information Data‚ information & knowledge Data Data consist of raw facts and figures - it does not have any meaning until it is processed and turned into something useful. It comes in many forms‚ the main ones being letters‚ numbers‚ images‚ symbols and sound. It is essential that data is available because it is the first
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A Seminar Report on BUSINESS INTELLIGENCE Prepared by: Guided By: Arpan Solanki Prof.Yagnik A. Rathod 100410107063 Assistant professor TY C.E SVIT-VASAD Certificate
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Chapter 11: Business Intelligence and Knowledge Management Data Mining and Online Analysis * Data warehouses are useless without software tools * Process data into information * Business intelligence (BI): information gleaned with information tools Data Mining * Data mining: selecting‚ exploring‚ and modeling data * * Supports decision making * Finds relationships and ratios within data * Finds unknown relationships * * Queries are more
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Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values‚ outliers‚ incorrect values‚ duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for
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Effective Business Intelligence for SME As smaller firms face competition and grow‚ it’s imperative they make good decisions based on even better information When asked what business intelligence (BI) tools are used to measure their organizational performance‚ the common response by entrepreneurs might include Excel spreadsheets‚ report writers and canned reports. BI can be defined as the ability to extract actionable insight from data available to the organization‚ both internal and external‚
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Data & Knowledge Engineering Introduction Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKEreaches a world-wide audience of researchers‚ designers‚ managers and users. The major aim of the journal is to identify‚ investigate and analyze the underlying principles in the design and effective use of these systems.DKE achieves this aim
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