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Execution-MiH ENCYCLOPEDIA →
Enterprise Intelligence →
SECTION - KDD-Data Mining →
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CHAPTER - KDD- Data Mining Overview
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Knowledge Discovery in Database is next level of evolution beyond data query and analytics. It moves from 'what has happened'/'why it has happened' to 'what is likely to happen?'. In tradition analytics, you have to ask right questions to get the right answers. In KDD- Data Mining, it helps you to ask right questions. KDD-Data mining moves from 'retroactive' to 'pro-active'.
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| Knowledge Discovery in Database is next level of evolution beyond data query and analytics. It moves from 'what has happened'/'why it has happened' to 'what is likely to happen?'. In tradition analytics, you have to ask right questions to get the right answers. In KDD- Data Mining, it helps you to ask right questions. KDD-Data mining moves from 'retroactive' to 'pro-active'. |
Topics
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What is KDD- Data Mining?
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Data Mining term is used interchangeably with KDD. In reality, it is one of the steps in the whole process of knowledge discovery in databases. Data Mining needs a well defined business case and a diligent data preparation and has to be followed with a detailed evaluation of the discovery results.
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Knowledge Discovery in Databases Program
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KDD - Data Mining program has two streams of requirements. Business/Functional requirements are centered around growth in revenue & profitability and business process optimization. Non-functional requirements like high response time, accuracy, visualization, metadata management and data quality ensure a continued sponsorship of a KDD program.
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Knowledge Discovery in Databases Process
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Knowledge Discovery in Databases comprises four key stages in an iterative flow- Business Case Definition, Data Preparation, Data Mining and Evaluation. Data Mining has no value on a stand-alone basis. Its success depends on how well you define the problem and on the level of diligence in data preparation.
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Data Mining Technology
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Data Mining architecture has three layers- Database Layer with sub-layers of database & metadata, application layer performing data management & algorithms and front-end layer for administration, input parameter settings and results display/visualization.
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KDD- Data Mining Issues & Challenges
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Key issues around KDD-Data Mining are around limited information, noisy & missing data, level of uncertainty and dynamically & fast-changing data reference.
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Knowledge Discovery in Databases Methodology
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Knowledge Discovery in Databases (KDD) methodology comprises five phases- Definition (vision/ business case/tools), specification (requirements/techniques/data analysis), design (data preparation/business case model), build and deploy.
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Data Mining Techniques- Propensity Modeling
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Data Mining Propensity modeling works on discovering a natural inclination or tendency across the variables. This group of techniques includes Cluster Analysis, Association Analysis and Sequential/Temporal patterns
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Data Mining Techniques- Predictive Modeling
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Predictive modeling is used to create a model of future or expected behavior. This group of data mining techniques includes Induction, Classification, Regression and Neural Networks.
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All Chapters in "KDD-Data Mining." Section
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KDD- Data Mining Overview → |
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