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\subsection{Clustering}  One natural experiment after obtaining data corresponding to all the pairwise combinations of songs in a dataset, is to attempt clustering. I used the Markov Cluster Algorithm (MCL)\footnote{http://micans.org/mcl/} with custom-tuned parameters to attempt to find groups of Billboard 2014 songs that were connected well with one another. After some experimentation with parameters, the largest cluster result returned contained:\\  \{\textit{Call Me Maybe}\text{ by Carly Rae Jepsen}, \textit{Heart Attack}\text{ by Demi Lovato}, \textit{Whistle}\text{ by FloRide}, \textit{Cruise}\text{ by Florida Georgia Line}, \textit{Stereo Hearts ft. Adam Levine}\text{ by Gym Class Heroes}, \textit{The Edge of Glory}\text{ by Lady Gaga}, \textit{Live While We're Young}\text{ by One Direction}, \textit{Try}\text{ by P!nk}, and \textit{If I Die Young}\text{ by The Band Perry}\}  \subsection{Visualization}  \subsection{Ranking Fully Connected Pairwise Comparisons}  \subsection{Ranking Random N-Gram Search}  \subsection{Key-Finding Accuracy}  \section{Smith-Waterman Results}  \subsection{Normalization}  \item Problem, just given a raw score independent of song length or anything else  \item Attempt to solve: $normalization$ $equation$ $used$  \subsection{Clustering}  \item Tried out using graph clustering algorithm  \item Future: try k-means  \section{Visualization}  \subsection{Motivation}  \item Given over 100 pairwise (triangle) number comparisons between sets of songs, it is important to have a useful means of visualizing the data  \item Initially tried using Graphos and Circos and other graphing libraries  \item Demanded a level of interactivity unachievable with other programs  \subsection{Using D3.js}  D3, a Javascript library, if used to create the visualization. All songs are displayed as radial nodes around a circle, and pairwise connections as edges.  \item Using popular javascript graphing library, can construct a visualization based on hierarchical edge bundling, as used in other modern graphing libraries (curved edges using tension factors)  \item Can add interactivity using web server  \subsection{Using Twistd webserver}  \item Facile, easy use web server all in Python. Link C libraries to Python and have a rapid coding environment that has fast native C-code at its base  \item Can compare align results in a visual interface based on reconstructing the aligned data  \subsection{Audio playback}  \item Audio transposition problem, and solution using fewest localized transpositions  \item Following along using popcorn.js  \subsection{Public implementation}  \item Webserver available at chordmatch.com