Introduction to Clustering Algorithms
Clustering algorithms are widely used in the analysis of Enterprise Big Data sets. They are applied for customer segmentation, targeted marketing and inventory mapping. In order to cluster data, algorithms will determine similarities (or dissimilarities) between individual observations. In this webinar, we will provide an introductory overview of common clustering algorithms and the way they are used in practice.
Clustering is the process of putting similar data into groups and is frequently used in data ming. A clustering algorithm partitions the observations in a data set into several groups in such a way that the similarity (in terms of variables) within a group is larger than between the different groups. Finding these partitions of data sets can have significant value for Enterprise organizations by enhancing their decision-making process.
There are a variety of different clustering techniques and corresponding algorithms. In this introductory webinar about clustering algorithms, we will consider some of the most popular examples. Most notably, we will consider the K-Means algorithm and Hierarchical clustering. We will discuss the (mathematical) process and underlying theory of these algorithms, and subsequently illustrate them in the R or Python languages.
This webinar will provide you with an introductory overview of clustering algorithms, explained by Jan-Willem Middelburg, the lead author of the Enterprise Big Data Framework. Even if you don’t have any background in data analysis or data science, you will be able to participate in this webinar. All the examples have been prepared online, so you can practice with the materials during and after the webinar. Additionally, all registered participants will receive access to the webinar presentation and relevant data analysis files. These will be made available shortly after the session.