# DATA MINING TUTORIAL PDF

Data Mining Tutorial in PDF - Learn Data Mining in simple and easy steps starting from basic to advanced concepts with examples Overview, Tasks, Data Mining. This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining. Prerequisites. In other words, we can say that data mining is the procedure of mining knowledge from data. . Here in this tutorial, we will discuss the major issues regarding −.

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PDF | On Jan 1, , Graham Williams and others published A Data Mining Tutorial. Data mining is also called as Knowledge discovery, Knowledge extraction, data/ pattern analysis, information harvesting, etc. In this tutorial, you will learn-. Overview of data mining. Emphasis is placed on basic data mining concepts. Techniques for uncovering interesting data patterns hidden in large data sets.

Neural networks are one of these techniques and are excellent for classification and regression, especially when the attribute relationships are nonlinear.

The genetic algorithm is yet another machine learning technique. It simulates the natural evolution process by working with a set of candidates and a survival fitness function.

The survival function repeatedly selects the most suitable candidates for the next generation. Genetic algorithms can be used for classification and clustering tasks.

They can also be used in conjunction with other algorithms, for instance, helping a neural network to find the best set of weights among neurons. A database is the third technical source for data mining.

## Data Mining Related Interview Questions

We begin by discussing Markov Systems which have no actions and the notion of Markov Systems with Rewards. We then motivate and explain the idea of infinite horizon discounted future rewards. And then we look at two competing approaches to deal with the following computational problem: given a Markov System with Rewards, compute the expected long-term discounted rewards.

The two methods, which usually sit at opposite corners of the ring and snarl at each other, are straight linear algebra and dynamic programming.

In addition to these slides, for a survey on Reinforcement Learning, please see this paper or Sutton and Barto's book.

Reinforcement Learning.

It concerns the fascinating question of whether you can train a controller to perform optimally in a world where it may be necessary to suck up some short term punishment in order to achieve long term reward. The curse of dimensionality will be constantly learning over our shoulder, salivating and cackling. Biosurveillance: An example. We review methods described in other biosurveillance slides as applied to hospital admissions data from the Walkerton Cryptosporidium outbreak of Elementary probability and Naive Bayes classifiers.

This slide repeats much of the material of the main Probability Slide from Andrew's tutorial series, but this slide-set focusses on disease surveillance examples, and includes a very detailed description for non-experts about how Bayes rule is used in practice, about Bayes Classifiers, and how to learn Naive Bayes classifiers from data.

Spatial Surveillance. This tutorial discusses Scan Statistics, a famous epidemiological method for discovering overdensities of disease cases. Time Series Methods.

This tutorial reviews some elementary univariate time series methods, with a focus on using the time series for alerting when a sequence of observations is starting to behave strangely.

Introduction to algorithms for computer game playing. We describe the assumptions about two-player zero-sum discrete finite deterministic games of perfect information. We also practice saying that noun-phrase in a single breath. After the recovery teams have done their job we talk about solving such games with minimax and then alpha-beta search.

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*SPRING DATA BOOK*

We also discuss the dynamic programming approach, used most commonly for end-games. We also debate the theory and practice of heuristic evaluation functions in games. Zero-Sum Game Theory. Want to know how and why to bluff in poker? How games can be compiled down to a matrix form? And general discussion of the basics of games of hidden information? Then these are the slides for you.

It might help you to begin by reading the slides on game-tree search. Non-zero-sum Game Theory.

## Data Mining Tutorial in PDF

Auctions and electronic negotiations are a fascinating topic. These slides take you through most of the basic story of the assumptions, the formalism and the mathematics behind non-zero-sum game theory.

It might help you to begin by reading the slides on game-tree search and Zero-sum Game theory with Hidden information available from this same set of tutorials. In this tutorial we cover the definition of a multiplayer non-zero-sum game, domination of strategies, Nash Equilibia. We deal with discrete games, and also games in which strategies include real numbers, such as your bid in a two player double auction negotiation.

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We cover prisoner's dilemma, tragedy of the commons, double auctions, and multi-player auctions such as the first price sealed auction and the second price auction. Introductory overview of time-series-based anomaly detection algorithms. This simple tutorial overviews some methods for detecting anomalies in biosurveillance time series.

The slides are incomplete: verbal commentary from the presentation has not yet been included as explanatory textboxes. Please let me awm cs.

If I receive enough requests I will try to make both of the above available. AI Class introduction.

A very quick informal discussion of the different kinds of AI research motivations out there Search Algorithms. What is a search algorithm?

## Statistical Data Mining Tutorials

What job does it do and where can it be applied? We introduce various flavors of Breadth First Search and Depth First search and then looks at alternatives and improvements that include Iterative Deepening and Bidirectional Search. Then we look with furrowed brow at an idea called Best First Search.

This will be our first view of a search algorithm that is able to exploit a heuristic function. A-star Heuristic Search. The classic algorithm for finding shortests paths given an admissible heuristic.

The tutorial teaches concepts from the AI literature on Constraint Satisfaction. This is a special case of uninformed search in which we want to find a solution configuration for some set of variables that satisfies a set of constraints.

Example problems including graph coloring, 8-queens, magic squares, the Waltz algorithm for interpreting line drawings, many kinds of scheduling and most important of all, the deduction phase of minesweeper. The algorithms we'll look at include backtracking search, forward checking search and constraint propagation search. We'll also look at general-purpose heuristics for additional search accelerations.

Robot Motion Planning. We review some algorithms for clever path planning once we arrive in real-valued continuous space instead of the safe and warm discrete space we've been sheltering in so far. Data mining can be applied to relational databases, object-oriented databases, data warehouses, structured-unstructured databases, etc.

Data mining is used in numerous areas like banking, insurance companies, pharmaceutical companies etc. Patterns in Data Mining 1. Association The items or objects in relational databases, transactional databases or any other information repositories are considered, while finding associations or correlations.

Classification The goal of classification is to construct a model with the help of historical data that can accurately predict the value. It maps the data into the predefined groups or classes and searches for the new patterns. For example: To predict weather on a particular day will be categorized into - sunny, rainy, or cloudy.The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning. Neural networks are one of these techniques and are excellent for classification and regression, especially when the attribute relationships are nonlinear.

Results generated by the data mining model should be evaluated against the business objectives.

This optimization method is called Expectation Maximization EM. In some cases, there could be data outliers.

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