I'm trying to classify web traffic using clustering algorithms with my own C program, capturing packets with
In this article K-Means, DBSCAN and AutoClass algorithms were used to classify web traffic.
I tested my dataset with different implementations of K-Means and DBSCAN, yet it is unclear to me how these two algorithms deal with data:
in K-Means from a C clustering library, the dataset is a matrix of
double** where the rows are the points of observation and the columns are the features; this data struct suits me fine, in my code every row is a connection and every column is a feature (delay, or average dimension, or bps, ...) of it;
in DBSCAN from a git repo the dataset is an
int ** matrix;
in another DBSCAN repo there is no matrix of features like above, but a linked list of
struct point with
double x, y coordinates.
I'm confused about the dataset representation: in my program, for every connection (row) there are a number of features (columns), but this representation seems to not agree with a linked list of points like in the second implementation of DBSCAN: should I convert the K-Means dataset implemented as a matrix in a linked list of
struct point for DBSCAN?
I know this seems a CodeReview question but I want to figure this out.