@@ -104,19 +104,20 @@ From the illustrations in the former exercise of ranking observations, it is evi
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@@ -104,19 +104,20 @@ From the illustrations in the former exercise of ranking observations, it is evi
Regarding the K-Nearest Neighbor (KNN) density, it can be shown that "My Heart Will Go On" and "The Nearness of You" are the most probable outliers. Meanwhile, the Average Relative Density (ARD) has been used to just find the one outlier "Redbone", as the density of this observation is a drastic enough amount lower than the other observation.
Regarding the K-Nearest Neighbor (KNN) density, it can be shown that "My Heart Will Go On" and "The Nearness of You" are the most probable outliers. Meanwhile, the Average Relative Density (ARD) has been used to just find the one outlier "Redbone", as the density of this observation is a drastic enough amount lower than the other observation.
To find the most probable outliers in the dataset, the best cause of action is to compare the results of the three methods covered above. This comparison shows that the three different sorting methods for outlier detection find "My Heart Will Go On" and "The Nearness of You" to be the most probable outliers, as
To find the most probable outliers in the dataset, the best cause of action is to compare the results of the three methods covered above. This comparison shows that the three different sorting methods for outlier detection find "My Heart Will Go On" and "The Nearness of You" to be the most probable outliers, as the Gaussian Kernel Density and KNN Density have these probable outliers in common. "Redbone" could also be an outlier, as this observation has the lowest density in the ARD but is not noted as such in any of the other methods.
In this part of the report, the data has to be binarized in order to use the Apriori algorithm to find associations between observations. Since the data of this report is full of categorical variables, the data has been one-out-of-K encoded for different intervals of each variable.
\subsection{Apriori Algorithm for Frequent Itemsets and Association Rules}
\subsection{Apriori Algorithm for Frequent Itemsets and Association Rules}
\textit{Find the frequent itemsets and the association rules with high confidence based on the results of the Apriori algorithm.}
\textit{Find the frequent itemsets and the association rules with high confidence based on the results of the Apriori algorithm.}\\
\subsection{Interpretation of the Association Rules}
\subsection{Interpretation of the Association Rules}
\textit{Interpret the generated association rules.}
\textit{Interpret the generated association rules.}\\
