In the final video in our Data Mining Fundamentals series, we conclude our discussion of different visualization techniques for data exploration with scatter plots and contour plots. We will define each plot, and share examples of when you can use …
Read moreIn this Data Mining Fundamentals tutorial, we discuss different visualization techniques, starting with the most popular: histograms and box plots. We discuss the unique benefits of both, and provide examples of when you can use each for your data exploration …
Read moreIn this Data Mining Fundamentals tutorial, we continue our discussion on data exploration and visualization. We discuss measuring of center such as the median and mean, and look at measures of spread such as range and variance.
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Read moreIn this Data Mining Fundamentals tutorial, we continue our discussion on data exploration and visualization. We discuss summary statistics and the frequency and mode of an attribute. Summary statistics are numbers that summarize properties of data, and the frequency of …
Read moreIn this Data Mining Fundamentals tutorial, we introduce you to data exploration and visualization and what they are to data mining. Data exploration is visualization and calculation to better understand characteristics of data. We will tell you the key motivations …
Read moreIn this Data Mining Fundamentals tutorial, we continue our discussion on similarity and dissimilarity and discuss correlation and visually evaluating it. Correlation measures the linear relationship between objects, and to visually evaluate correlation, you will need to build a scatter …
Read moreIn this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix.
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Read moreIn this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also …
Read moreIn this Data Mining Fundamentals tutorial, we discuss the transformation of data in data preprocessing, such as attribute transformation. Attribute transformation is a function that maps the entire set of values of a given attribute to a new set of …
Read moreIn this Data Mining Fundamentals tutorial, we discuss another way of dimensionality reduction, feature subset selection. We discuss the many techniques for feature subset selection, including the brute-force approach, embedded approach, and filter approach. Feature subset selection will reduce redundant …
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