Caltech Stem

       My group is under Dr. Hassibi for Machine Learning. This semester we have studying Machine Learning but more specifically: graph clustering algorithms. We tested a lot of algorithms on Zachary's Karate Dataset but what was more important than implementing them was understanding them. A lot of the algorithms were math intensive so in order to really understand why algorithms work the way they do, we focused on explaining the math. In order to see how many options there are to cluster the Karate dataset, algorithms were assigned after each meeting. Some algorithms that I did were Spectral Clustering, Girvan Newman, and more recently Fuzzy c-means. Karate Club Dataset        Aside from learning new algorithms, I think my Caltech experiences have helped me improve in other ways. For instance, when I began, I did not know a lot of linear algebra, but after studying math-centered subjects for a couple months, I have gotten into the habit of teaching myself it. I also kno

These past few weeks have been slow. Dr. Hassibi could not come to the last meeting and we had a school break. In the meantime, we started to learn a new algorithm that did not have to do with graph clustering. I studied Fuzzy C-Means which was fairly easy to understand. Fuzzy c-means (FCM) is a method of clustering that allows points to be in more than one cluster. This method was developed by Dunn in 1973 and improved by Bezdek in 1981. The problem of classifying outliers gave rise to non- hard algorithms that were flexible enough to effectively deal with these points, hence the name “ soft clustering ”. It is also known as “ soft k-means ” due to its design centered around k-means. A big difference between the two algorithms is that FCM assigns a membership of every node to each cluster, enabling points to be in two or more clusters. Below is an image of FCM being used to do image partitioning. After is my video explaining what FCM is in further detail. https://youtu