11 Advanced Analytical Tasks Comparative and relative analysisException and trend analysisTime series analysisForecastingWhat-if analysisModelingSimultaneous equations
12 Analytical Suites Enterprise business intelligence (EBI) toolsets: - Web-enabled query, reporting, andanalysis tool that runs on a robustapplication server- EBI toolset tightly integrates query,reporting, and analysis capabilities within asingle tool- Shares a common look and feelBusiness portals:- EBI toolset with a Yahoo-like user interface- Flexible repository handles structured andunstructured data objects.
13 Data Mining ToolsIdentify patterns and relationships in data that are often useful for building models that aid decision making or predict behaviorData mining uses technologies such as neural networks, rule induction, and clustering to discover relationships in data and make predictions that are hidden, not apparent, or too complex to be extracted using statistical techniques.
14 Analytical Applications Packaged analytical application has a predefined:- Extraction feeds and transformationroutines for a specific data source- Data model, application-specificreport templates, and a custom end-user interface.Custom analytic applications are workbenches that enable developers to quickly create analytic applications from coarse-grained components, including user interface widgets, data access and analysis components, and report layouts.
15 Definition of Data Mining “ Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns, trends, relationships, and rules. ”Data mining is also known as:Knowledge discoveryData surfingData harvesting
16 Use of Data Mining Customer profiling Market segmentation Buying pattern affinitiesDatabase marketingCredit scoring and risk analysis
17 Functions of Data Mining Discovers facts and data relationshipsFinds patternsDetermines rulesRetains and reuse rulesPresents information to usersMay take many hoursRequires knowledgeable people to analyze the results
18 Comparing DSS and Data Mining Queries DSS queries:- Based on prior knowledge andassumptions- User-drivenData mining queries:- Require domain-specific knowledgeto interpret data- User-guided
19 Artificial Neural Networks Predictive model that learnsDeveloped from understand of the human brainMultiple regression and other statistical techniques15268374
20 Decision Trees Represent decisions Generate rules Classify Annual salary100,000AnnualoutgoingAnnualcredit<10,000>50,000GoodBad
21 Other Techniques Genetic algorithms based on evolution theory Statistics such as averages and totalsNearest neighbor to find associationsRules induction applying IF-THEN logicExperiment with different techniques
22 AssociatesWhich items are purchased in a retail store at the same time?
23 Sequential Patterns What is the likelihood that a customer will buy a product next month, if he buys a related item today?
24 Classifications Determine customers’ buying patterns and then find other customers withsimilar attributes that may be targeted fora marketing campaign.
25 Modeling Use factors, such as location, number of bedrooms, and square footage, toDetermine the market value of a property
27 Summary This lesson covered the following topics: Describing the importance of business intelligenceIdentifying where data mining might be employed in a warehouse environmentIdentifying data mining tools