The unsupervised learning algorithm k-means requires one input parameter: the k. Given k and your data, the algorithm returns an answer which labels each element of your data to a cluster. The algorithm finds k 'centroids' and points are assigned to the nearest centroid. The point of the algorithm is to find the best set of centroids given the data.
This episode is an overview of the topic presented in several segments.
In today’s episode, Gregory Glatzer explained his machine learning project that involved the prediction of elephant movement and settlement, in a bid to limit the activities of poachers. He used two machine learning algorithms, DBSCAN and K-Means clustering at different stages of the project. Listen to learn about why these two techniques were useful and what conclusions could be drawn.
Linh Da joins us to explore how image segmentation can be done using k-means clustering. Image segmentation involves dividing an image into a distinct set of segments. One such approach is to do this purely on color, in which case, k-means clustering is a good option.
Have you ever wondered how you can use clustering to extract meaningful insight from a time-series single-feature data? In today’s episode, Ehsan speaks about his recent research on actionable feature extraction using clustering techniques. Want to find out more? Listen to discover the methodologies he used for his research and the commensurate results.
In this episode, Kyle interviews Lucas Murtinho about the paper "Shallow decision treees for explainable k-means clustering" about the use of decision trees to help explain the clustering partitions.
In today’s episode, Jason, an Assistant Professor of Statistical Science at Duke University talks about his research on K power means. K power means is a newly-developed algorithm by Jason and his team, that aims to solve the problem of local minima in classical K-means, without demanding heavy computational resources. Listen to find out the outcome of Jason's study.
In this episode, we speak with Bernd Fritzke, a proficient financial expert and a Data Science researcher on his recent research - the breathing K-means algorithm. Bernd discussed the perks of the algorithms and what makes it stand out from other K-means variations. He extensively discussed the working principle of the algorithm and the subtle but impactful features that enables it produce top-notch results with low computational resources. Listen to learn about this algorithm.
Many people know K-means clustering as a powerful clustering technique but not all listeners will be as familiar with spectral clustering. In today’s episode, Sibylle Hess from the Data Mining group at TU Eindhoven joins us to discuss her work around spectral clustering and how its result could potentially cause a massive shift from the conventional neural networks. Listen to learn about her findings.
In this episode, we interview Jonas Landman, a Postdoc candidate at the University of Edinburg. Jonas discusses his study around quantum learning where he attempted to recreate the conventional k-means clustering algorithm and spectral clustering algorithm using quantum computing.