Data Skeptic

Breathing K-Means

Mar 14, 2022

42:55

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Highlights

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Episode notes

Is Camins Plus a Better Approach to Machine Learning?

01:40

Is There a Free Lunch for Optimization?

05:26

Is Camens Really a Data Reduction Technique?

01:43

Introduction

2min

Is Camens Really a Data Reduction Technique?

2min

Is Camins Plus a Better Approach to Machine Learning?

2min

What Happens When You Get a Bad Augorism?

3min

Algarism - How to Add Centuris to the Existing Set?

2min

How to Add Sentreeds to the Leuts Algerism?

4min

Camis Plus Plus - How Much Iterations Will It Take?

3min

Is There a Problem of Camins Plus Plus?

2min

Is Camens a Good Clustering Method?

4min

10.

M Particle - Your Real Time Customer Data Interstructure to Accelerate Your Customer Data Strategy

3min

11.

Is There a Free Lunch for Optimization?

5min

12.

Clustering Algorithms

2min

13.

Algarism - The Choice of Centreds to Delead

3min

14.

Breasin Cemis

2min

15.

How to Implement Breathing Camins in Python?

3min

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.