Adaptive business intelligence ppt

What is Business Intelligence and application of business intelligence ppt and adaptive business intelligence by zbigniew michalewicz ppt
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MasonHarper,United States,Teacher
Published Date:17-07-2017
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Topics in Business Intelligence Lecture 1: Introduction to BI & case study Tommi Tervonen Econometric Institute, Erasmus University RotterdamWhat is Business Intelligence (BI)? BI refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes. BI technologies provide historical, current, and predictive views of business operations. Business Intelligence often aims to support better business decision-making. of BIExamples of BIExamples of BIExamples of BIBI framework Watson & Wixom, 2007Main components in BIKnowledge discovery processWhy data mining? Tremendous amount of data Walmart Customer buying patterns a data warehouse 7.5 Terabytes large in 1995 VISA Detecting credit card interoperability issues 6800 payment transactions per second High dimensionality of data Many dimensions to be combined together High complexity of data Time-series data, temporal data, sequence data Spatial, spatiotemporal, multimedia, text and Web dataData mining Subtypes: Text mining: mining of patterns from text Web mining: discovering patterns from the webData mining: predictive analysis types Classi cation of observations to (possibly ordered) classes, e.g. credit card transactions to normal or fraudulent ones. Prediction is similar, but instead of assignment to classes, we try to predict the value of a numerical variable, e.g. amount of credit card purchase. Association rules or anity analysis tells what is associated with the observations. Recommender systems (e.g. use association rules.Data mining: pre-analyses Data visualization allows \easy" overview of the data. Data exploration often needs to be done with large data sets to answer more vague questions. Similar variables and observations can be aggregated to get a better picture of the data. Data reduction consolidates a large number of variables or cases into a smaller set. Correlation & principal component analyses.What is 'data'? Data can essentially be: 1 Continuous ordered values with a scale. E.g. client monthly spending (e), speed of car (km/h) 2 Categorical discrete, possibly ordered values. E.g. car class (small family car, large family car, executive, ...), bank customer credit class (A, B, C, D) Often data is categorical due to form of reporting (e.g. from questionnaires: monthly salary)Data mining methods for BI Mostly: Statistical methods for analysis of continuous variables Machine learning for analysis of categorical variables Variables are divided into predictors and responsesData nature & methods Continuous Categorical No response response response Continuous Linear regression Logistic regression Principal components predictors Neural nets Neural nets Cluster analysis k-nearest neighbors Discriminant analysis k-nearest neighbors Categorical Linear regression Neural nets Association rules predictors Neural nets Classi cation trees Regression trees Logistic regression Naive Bayes Ordered categorical variables (e.g. 1, 2, 3) can often be converted to continuous ones Continuous variables can always be converted to categorical ones through frequency analysis (binning)Data mining processLearning modes In unsupervised learning, no outcome variable is predicted.Learning modes In supervised learning the model is trained to predict a known response. The data needs to be split into training and test sets.Supervised learning with linear regression x = 200, y =?