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Data Mining

Study the algorithms for

Supervised Learning

Study the algorithms for

Unsupervised Learning



  • Weko 3

    Open source, java, tools for data pre-processing, classification, regression, clustering, association rules, visualization

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  • Data Mining with Weka

    Principles of popular algorithms

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  • Guide to data mining Textbook

    Recommendation systems, classification, Naïve Bayes, clustering

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  • Data Mining Concepts and Techniques Non-fictional

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  • Konwledge Discovery in Databases Techniken und Anwendungen Non-fictional

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  • Data Science For Business, What You Need to Know about Data Mining Non-fictional

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  • Scikit-Learn Software libray

    Classification, regression, clustering, dimensionality reduction, model selection, preprocessing, built on NumPy, SciPy, and matplotlib

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  • Statistical classification

    The problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known.

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  • Bias-variance tradeoff

    The conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set

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  • K means clustering Method

    k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster

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  • Curse of dimensionality

    Phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings

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  • Cluster Analysis

    Task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters)

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  • Principal component analysis Technique

    Dimensionality reduction, statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components, condense the information of a large set of correlated variables into a few variables

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  • Decision tree

    a flowchart-like structure in which each internal node represents a "test" on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label, the paths from root to leaf represent classification rules.

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  • Association rule learning

    A rule-based machine learning method for discovering interesting relations between variables in large databases

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