Skip to main content

DWDM lab exp2

 

  1. Explore machine learning tool“WEKA”//2.a//Explore WEKA Data Mining/Machine Learning Toolkit. ANS:

WEKA(Waikato Environment for Knowledge Analysis) an open-source software provides tools for data preprocessing, implementation of several Machine Learning algorithms, and visualization tools so that we can develop machine learning techniques and apply them to real-world data mining problems.

Features of WEKA -

  1. Preprocessor – Most of the Data is Raw. Hence, Preprocessor is used to clean the noisy data.

  2. Classify – After preprocessing the data, we assign classes or categories to items.

  3. Cluster – In Clustering, a dataset is arranged in different groups/clusters based on some similarities.

  4. Associate – Association rules highlight all the associations and correlations between items of a dataset.

  5. Select Attributes – Every dataset contains a lot of attributes; only significantly valuable attributes are selected for building a good model.

  6. Visualize – In Visualization, different plot matrices and graphs are available to show the trends and errors identified by the model.




Comments

Popular posts from this blog

DWDM (Basic statistical description of data)

Hashing and hash functions

Types of hash functions

Previous year question papers Data structures