Artículo: AMZ-B0CW1K6JPW

AN INTRODUCTION TO DATA WAREHOUSING & DATA MINING

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UNIT 1: Data WarehouseDefinition & Purpose: A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making.Architecture: Involves source systems, ETL processes, data storage (fact and dimension tables), metadata, and tools for data access.Data Modeling: Includes star schema, snowflake schema, and fact constellation.OLAP Operations: Roll-up, drill-down, slice, dice, pivot, and drill-across are key for analyzing multidimensional data.Concept Hierarchies & Measures: Support aggregation, generalization, and summarization for analytical purposes.UNIT 2: Introduction to Data MiningDefinitionFunctionalities: Classification, clustering, association rules, regression, outlier detection, and sequential pattern mining.Tasks:Trends:ChallengesUNIT 3: Association and Correlation AnalysisAssociation Rule Learning: An unsupervised method to find interesting relationships between variables (e.g., Market Basket Analysis).Key Algorithms:Apriori: Generates frequent itemsets using support and confidence thresholds.FP-Growth: A faster alternative to Apriori that avoids candidate generation.Evaluation Metrics: Support, Confidence, and Lift.Applications: Retail product bundling, fraud detection, web usage analysis.UNIT 4: Clustering Algorithms and Cluster AnalysisDefinition:Algorithms:K-Means: Partitions data into K clusters based on centroid distance.K-Medoids (PAM): Uses medoids instead of centroids; less sensitive to outliers.Hierarchical Clustering: Forms a tree (dendrogram); includes agglomerative and divisive approaches.Graph-Based Clustering: Uses connectivity in graphs to form clusters.Evaluation: Based on intra-cluster similarity and inter-cluster dissimilarity.Applications: Customer segmentation, document classification, anomaly detection.UNIT 5: ClassificationSupervised Learning: Models are trained with labeled data to classify new data.Types:Binary vs Multiclass ClassificationTechniques:Naive Bayes, K-NN, Decision Trees, SVM, Neural Networks, Rule-Based Classifiers, Ensemble Methods.Evaluation Metrics: Accuracy, Precision, Recall, F1-score.Applications: Email spam detection, medical diagnosis, risk prediction, fraud detection.UNIT 6: Web MiningDefinition: Applying data mining techniques to extract information from the World Wide Web.Types:Web Content Mining: Extracting useful content (text, images, multimedia).Web Structure Mining: Analyzing the link structure (e.g., PageRank, HITS).Web Usage Mining: Understanding user behavior through logs and sessions.Applications: E-commerce, search engines etc.Tools: Scrapy, BeautifulSoup, NLTK, Weka, Spark.Challenges: Scalability, dynamic data, noise, privacy concerns