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利用Weka进行深度数据挖掘 | MOOC中国 - 慕课改变你,你改变世界


More Data Mining with Weka

Learn more about practical data mining, including how to deal with large data sets. Use advanced techniques to mine your own data.

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  • 完成时间大约为 5
  • 初级
  • 英语


Compare the performance of different mining methods on a wide range of datasets

Demonstrate how to set up learning tasks as a knowledge flow

Solve data mining problems on huge datasets

Apply equal-width and equal-frequency binning for discretizing numeric attributes

Identify the advantages of supervised vs unsupervised discretization

Evaluate different trade-offs between error rates in 2-class classification

Classify documents using various techniques

Debate the correspondence between decision trees and decision rules

Explain how association rules can be generated and used

Discuss techniques for representing, generating, and evaluating clusters

Perform attribute selection by wrapping a classifier inside a cross-validation loop

Describe different techniques for searching through subsets of attributes

Develop effective sets of attributes for text classification problems

Explain cost-sensitive evaluation, cost-sensitive classification, and cost-sensitive learning

Design and evaluate multi-layer neural networks

Assess the volume of training data needed for mining tasks

Calculate optimal parameter values for a given learning system


This course introduces advanced data mining skills, following on from Data Mining with Weka. You’ll process a dataset with 10 million instances. You’ll mine a 250,000-word text dataset. You’ll analyze a supermarket dataset representing 5000 shopping baskets. You’ll learn about filters for preprocessing data, selecting attributes, classification, clustering, association rules, cost-sensitive evaluation. You’ll meet learning curves and automatically optimize learning parameters. Weka originated at the University of Waikato in NZ, and Ian Witten has authored a leading book on data mining.


Running large-scale data mining experiments

Constructing and executing knowledge flows

Processing very large datasets

Analyzing collections of textual documents

Mining association rules

Preprocessing data using a range of filters

Automatic methods of attribute selection

Clustering data

Taking account of different decision costs

Producing learning curves

Optimizing learning parameters in data mining


This course is aimed at anyone who deals in data. It follows on from Data Mining with Weka, and you should have completed that first (or have otherwise acquired a rudimentary knowledge of Weka). As with the previous course, it involves no computer programming, although you need some experience with using computers for everyday tasks. High-school maths is more than enough; some elementary statistics concepts (means and variances) are assumed.

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