0 bookmark(s) - Sort by: Date ↓ / Title /
The article discusses the evolution of search databases and how vector databases are emerging as a powerful alternative to traditional search engines like Elasticsearch.
ow can you learn about the underlying structure of documents in a way that is informative and intuitive? This basic motivating question led me on a journey to visualize and cluster documents in a two-dimensional space. What you see above is an output of an analytical pipeline that begin by gathering synopses on the top 100 films of all time and ended by analyzing the latent topics within each document. In between I ran significant manipulations on these synopses (tokenization, stemming), transformed them into a vector space model (tf-idf), and clustered them into groups (k-means). You can learn all about how I did this with my detailed guide to Document Clustering with Python. But first, what did I learn?
tokenizing and stemming each synopsis transforming the corpus into vector space using tf-idf calculating cosine distance between each document as a measure of similarity clustering the documents using the k-means algorithm using multidimensional scaling to reduce dimensionality within the corpus plotting the clustering output using matplotlib and mpld3 conducting a hierarchical clustering on the corpus using Ward clustering plotting a Ward dendrogram topic modeling using Latent Dirichlet Allocation (LDA)
First / Previous / Next / Last
/ Page 1 of 0