Contents
What kind of text is outliers?
Outliers is journalistic narrative non-fiction. It recounts examples of people throughout history who were “outliers” of the population in terms of success and provides an analysis of these situations.
How is an outlier related to a cluster?
Since most of the clustering algorithms have a minimum threshold of data points to form a cluster, the outliers are the lone data points that are not clustered. Even if the outliers form a cluster, they are far away from other clusters.
What makes you an outlier?
An outlier is a person who is detached from the main body of a system. An outlier lives a rather special life compared to the majority of people.
How is outlier Aware Clustering used in data science?
This involves: Redefining the space in which the vectors exist by defining a new embedding space on a Riemannian manifold using UMAP; Using HDBSCAN to cluster close but not necessarily spherical clusters together, while ignoring outliers.
How to choose the optimal number of clusters?
We will choose a varying number of clusters (usually a slider from 1 to 20). Using the elbow method, we will find the optimal number of clusters. To confirm that the elbow method assist in deciding the number of clusters, we will use the silhouette value so see how well each point fits in its target cluster.
When to use k-means for clustering data?
On typical numerical or categorical data, K-Means makes a lot of sense for creating clusters. We can also use this approach a lot when separating simple word embeddings (1 to 4 words), but it loses signal when combining vectors of strings, where the cosine similarities across word embeddings are much more similar.
How is clustering used in the labor market?
The ones that either already have high demand, or those that are growing. A labor market analysis is the standard approach to determine what are the job market trends. This will give us an indication of job demands over time. Then, we will group each job into a specific category.