KDD In Data Mining
Knowledge Discovery in Databases (KDD) is the comprehensive process of extracting meaningful, valid, and actionable knowledge from large datasets. While data mining is a core step within KDD, the overall KDD process includes data selection, cleaning, integration, transformation, data mining, pattern evaluation, and knowledge presentation. KDD transforms raw data into understandable insights that support decision-making, predictive modeling, and strategic planning. It is widely applied in business intelligence, healthcare analytics, finance, marketing, cybersecurity, and scientific research. By combining statistical methods, machine learning techniques, and database systems, KDD enables organizations to uncover hidden patterns, correlations, trends, and predictive relationships from structured and unstructured data.
Knowledge Discovery in Databases, KDD Process, Data Mining, Data Preprocessing, Data Cleaning, Data Integration, Data Transformation, Pattern Discovery, Predictive Modeling, Descriptive Analytics, Machine Learning, Big Data Analytics, Feature Engineering, Pattern Evaluation, Business Intelligence, Data Visualization
#KDD#KnowledgeDiscovery#DataMining#MachineLearning#BigDataAnalytics#DataScience#PredictiveAnalytics#BusinessIntelligence#DataPreprocessing#PatternRecognition
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