Data Mining Specialization

Description

Data Mining Specialization

Content
01_1-1-1-some-books-on-data-visualization
01_1-1-overview-text-mining-and-analytics-part-1
01_1-2-1-2-d-graphics
01_1-3-1-the-human
01_2-1-1-data
01_2-1-syntagmatic-relation-discovery-entropy
01_2-2-1-glyphs-part-1
01_2-3-1-tuftes-design-rules
01_3-1-1-graphs-and-networks
01_3-1-probabilistic-topic-models-mixture-of-unigram-language-models
01_3-2-1-principal-component-analysis
01_3-3-1-packing
01_4-1-1-visualization-systems
01_4-1-text-clustering-motivation
01_4-2-1-visualization-system-design
01_5-1-text-categorization-discriminative-classifier-part-1
01_6-1-opinion-mining-and-sentiment-analysis-latent-aspect-rating-analysis-part-1
01_course-introduction
01_course-introduction
01_course-welcome-video
01_introduction-to-text-mining-and-analytics
01_lesson-1-1-natural-language-content-analysis
01_lesson-2-1-vector-space-model-improved-instantiation
01_lesson-3-1-evaluation-of-tr-systems
01_lesson-4-1-probabilistic-retrieval-model-basic-idea
01_lesson-5-1-feedback-in-text-retrieval
01_lesson-6-1-learning-to-rank-part-1
01_welcome-to-the-data-mining-project
02_1-1-2-overview-of-visualization
02_1-1-what-is-cluster-analysis
02_1-1-what-is-pattern-discovery-why-is-it-important
02_1-2-overview-text-mining-and-analytics-part-2
02_1-3-2-memory
02_2-1-2-mapping
02_2-1-basic-concepts-measuring-similarity-between-objects
02_2-1-the-downward-closure-property-of-frequent-patterns
02_2-2-1-glyphs-part-2
02_2-2-syntagmatic-relation-discovery-conditional-entropy
02_2-3-2-using-color
02_3-1-2-embedding-planar-graphs
02_3-1-limitation-of-the-support-confidence-framework
02_3-1-partitioning-based-clustering-methods
02_3-2-2-multidimensional-scaling
02_3-2-probabilistic-topic-models-mixture-model-estimation-part-1
02_4-1-2-the-information-visualization-mantra-part-1
02_4-1-hierarchical-clustering-methods
02_4-1-mining-multi-level-associations
02_4-2-text-clustering-generative-probabilistic-models-part-1
02_4-6-cure-clustering-using-well-scattered-representatives
02_5-1-density-based-and-grid-based-clustering-methods
02_5-1-sequential-pattern-and-sequential-pattern-mining
02_5-2-text-categorization-discriminative-classifier-part-2
02_6-1-methods-for-clustering-validation
02_6-1-mining-spatial-associations
02_6-2-opinion-mining-and-sentiment-analysis-latent-aspect-rating-analysis-part-2
02_7-1-from-frequent-pattern-mining-to-phrase-mining
02_8-1-frequent-pattern-mining-in-data-streams
02_clustereng-overview
02_course-introduction-video
02_course-prerequisites-completion
02_lesson-1-2-text-access
02_lesson-2-2-tf-transformation
02_lesson-3-2-evaluation-of-tr-systems-basic-measures
02_lesson-4-2-statistical-language-model
02_lesson-5-2-feedback-in-vector-space-model-rocchio
02_lesson-6-2-learning-to-rank-part-2
02_svg-example
02_week-1-introduction
02_week-2-introduction
02_week-3-introduction
02_week-4-introduction
03_1-2-2-2-d-drawing
03_1-2-applications-of-cluster-analysis
03_1-2-frequent-patterns-and-association-rules
03_1-3-3-reasoning
03_1-3-natural-language-content-analysis-part-1
03_2-1-3-charts
03_2-2-2-parallel-coordinates
03_2-2-distance-on-numeric-data-minkowski-distance
03_2-2-the-apriori-algorithm
03_2-3-syntagmatic-relation-discovery-mutual-information-part-1
03_3-1-3-graph-visualization
03_3-2-interestingness-measures-lift-and-kh2
03_3-2-k-means-clustering-method
03_3-3-probabilistic-topic-models-mixture-model-estimation-part-2
03_4-1-2-the-information-visualization-mantra-part-2
03_4-2-agglomerative-clustering-algorithms
03_4-2-mining-multi-dimensional-associations
03_4-3-text-clustering-generative-probabilistic-models-part-2
03_4-7-chameleon-graph-partitioning-on-the-knn-graph-of-the-data
03_5-2-dbscan-a-density-based-clustering-algorithm
03_5-2-gsp-apriori-based-sequential-pattern-mining
03_5-3-text-categorization-evaluation-part-1
03_6-2-clustering-evaluation-measuring-clustering-quality
03_6-2-mining-spatial-colocation-patterns
03_6-3-text-based-prediction
03_7-2-previous-phrase-mining-methods
03_8-2-pattern-discovery-for-software-bug-mining
03_clustereng-k-means-and-k-medoids
03_lesson-1-3-text-retrieval-problem
03_lesson-2-3-doc-length-normalization
03_lesson-3-3-evaluation-of-tr-systems-evaluating-ranked-lists-part-1
03_lesson-4-3-query-likelihood-retrieval-function
03_lesson-5-3-feedback-in-text-retrieval-feedback-in-lm
03_lesson-6-3-learning-to-rank-part-3
04_1-2-3-3-d-graphics
04_1-3-4-the-human-retina
04_1-3-compressed-representation-closed-patterns-and-max-patterns
04_1-3-requirements-and-challenges
04_1-4-natural-language-content-analysis-part-2
04_2-2-3-stacked-graphs-part-1
04_2-3-extensions-or-improvements-of-apriori
04_2-3-proximity-measure-for-symetric-vs-asymmetric-binary-variables
04_2-4-syntagmatic-relation-discovery-mutual-information-part-2
04_3-1-4-tree-maps
04_3-3-initialization-of-k-means-clustering
04_3-3-null-invariance-measures
04_3-4-probabilistic-topic-models-expectation-maximization-algorithm-part-1
04_4-1-2-the-information-visualization-mantra-part-3
04_4-3-divisive-clustering-algorithms
04_4-3-mining-quantitative-associations
04_4-4-text-clustering-generative-probabilistic-models-part-3
04_4-8-probabilistic-hierarchical-clustering
04_5-3-optics-ordering-points-to-identify-clustering-structure
04_5-3-spade-sequential-pattern-mining-in-vertical-data-format
04_5-4-text-categorization-evaluation-part-2
04_6-3-constraint-based-clustering
04_6-3-mining-and-aggregating-patterns-over-multiple-trajectories
04_6-4-contextual-text-mining-motivation
04_7-3-topmine-phrase-mining-without-training-data
04_8-3-pattern-discovery-for-image-analysis
04_clustereng-application-agnes
04_lesson-1-4-overview-of-text-retrieval-methods
04_lesson-2-4-implementation-of-tr-systems
04_lesson-3-4-evaluation-of-tr-systems-evaluating-ranked-lists-part-2
04_lesson-4-4-statistical-language-model-part-1
04_lesson-5-4-web-search-introduction-web-crawler
04_lesson-6-4-future-of-web-search
05_1-2-4-photorealism
05_1-3-5-perceiving-two-dimensions
05_1-4-a-multi-dimensional-categorization
05_1-5-text-representation-part-1
05_2-2-3-stacked-graphs-part-2
05_2-4-distance-between-categorical-attributes-ordinal-attributes-and-mixed-types
05_2-4-mining-frequent-patterns-by-exploring-vertical-data-format
05_2-5-topic-mining-and-analysis-motivation-and-task-definition
05_3-4-comparison-of-null-invariant-measures
05_3-4-the-k-medoids-clustering-method
05_3-5-probabilistic-topic-models-expectation-maximization-algorithm-part-2
05_4-1-3-database-visualization-part-1
05_4-4-extensions-to-hierarchical-clustering
05_4-4-mining-negative-correlations
05_4-5-text-clustering-similarity-based-approaches
05_5-4-grid-based-clustering-methods
05_5-4-prefixspan-sequential-pattern-mining-by-pattern-growth
05_5-5-opinion-mining-and-sentiment-analysis-motivation
05_6-4-external-measures-1-matching-based-measures
05_6-4-mining-semantics-rich-movement-patterns
05_6-5-contextual-text-mining-contextual-probabilistic-latent-semantic-analysis
05_7-4-segphrase-phrase-mining-with-tiny-training-sets
05_8-4-advanced-topics-on-pattern-discovery-pattern-mining-and-society-privacy
05_clustereng-application-dbscan
05_lesson-1-5-vector-space-model-basic-idea
05_lesson-2-5-system-implementation-inverted-index-construction
05_lesson-3-5-evaluation-of-tr-systems-multi-level-judgements
05_lesson-4-5-statistical-language-model-part-2
05_lesson-5-5-web-indexing
05_lesson-6-5-recommender-systems-content-based-filtering-part-1
06_1-2-5-non-photorealism
06_1-3-6-perceiving-perspective
06_1-5-an-overview-of-typical-clustering-methodologies
06_1-6-text-representation-part-2
06_2-5-fpgrowth-a-pattern-growth-approach
06_2-5-proximity-measure-between-two-vectors-cosine-similarity
06_2-6-topic-mining-and-analysis-term-as-topic
06_3-5-the-k-medians-and-k-modes-clustering-methods
06_3-6-probabilistic-topic-models-expectation-maximization-algorithm-part-3
06_4-1-3-database-visualization-part-2
06_4-5-birch-a-micro-clustering-based-approach
06_4-5-mining-compressed-patterns
06_4-6-text-clustering-evaluation
06_5-5-clospan-mining-closed-sequential-patterns
06_5-5-sting-a-statistical-information-grid-approach
06_5-6-opinion-mining-and-sentiment-analysis-sentiment-classification
06_6-5-external-measure-2-entropy-based-measures
06_6-5-mining-periodic-movement-patterns
06_6-6-contextual-text-mining-mining-topics-with-social-network-context
06_8-5-advanced-topics-on-pattern-discovery-looking-forward
06_lesson-1-6-vector-space-retrieval-model-simplest-instantiation
06_lesson-2-6-system-implementation-fast-search
06_lesson-3-6-evaluation-of-tr-systems-practical-issues
06_lesson-4-6-smoothing-methods-part-1
06_lesson-5-6-link-analysis-part-1
06_lesson-6-6-recommender-systems-content-based-filtering-part-2
07_1-6-an-overview-of-clustering-different-types-of-data
07_1-7-word-association-mining-and-analysis
07_2-6-correlation-measures-between-two-variables-covariance-and-correlation
07_2-6-mining-closed-patterns
07_2-7-topic-mining-and-analysis-probabilistic-topic-models
07_3-6-kernel-k-means-clustering
07_3-7-probabilistic-latent-semantic-analysis-plsa-part-1
07_4-1-3-database-visualization-part-3
07_4-7-text-categorization-motivation
07_5-6-clique-grid-based-subspace-clustering
07_5-7-opinion-mining-and-sentiment-analysis-ordinal-logistic-regression
07_6-6-external-measure-3-pairwise-measures
07_6-7-contextual-text-mining-mining-casual-topics-with-time-series-supervision
07_lesson-4-7-smoothing-methods-part-2
07_lesson-5-7-link-analysis-part-2
07_lesson-6-7-recommender-systems-collaborative-filtering-part-1
08_1-7-an-overview-of-user-insights-and-clustering
08_1-8-paradigmatic-relation-discovery-part-1
08_2-8-probabilistic-topic-models-overview-of-statistical-language-models-part-1
08_3-8-probabilistic-latent-semantic-analysis-plsa-part-2
08_4-8-text-categorization-methods
08_6-7-internal-measures-for-clustering-validation
08_6-8-course-summary
08_lesson-5-8-link-analysis-part-3
08_lesson-6-8-recommender-systems-collaborative-filtering-part-2
09_1-9-paradigmatic-relation-discovery-part-2
09_2-9-probabilistic-topic-models-overview-of-statistical-language-models-part-2
09_3-9-latent-dirichlet-allocation-lda-part-1
09_4-9-text-categorization-generative-probabilistic-models
09_6-8-relative-measures
09_lesson-6-9-recommender-systems-collaborative-filtering-part-3
10_2-10-probabilistic-topic-models-mining-one-topic
10_3-10-latent-dirichlet-allocation-lda-part-2
10_6-9-cluster-stability
10_lesson-6-10-course-summary
11_6-10-clustering-tendency