Eikosstatistics & data science

Machine Learning & Artificial Intelligence · Data Mining & Feature Engineering

Data mining: KDD and data preparation

Public introduction · full guide in Eikos

Introduction

What this guide covers

A mathematical, algorithmic and methodological approach to knowledge extraction, feature engineering and multidimensional data quality assurance.

Overview

Topics covered

  • Introduction
  • Data Mining: The KDD Process and Rigorous Data Preparation
  • The KDD Paradigm (Knowledge Discovery in Databases)
  • 1.1 Contextualization and Epistemological Evolution
  • 1.2 Systemic Architecture and Detailed Phases of the KDD Process
  • 1.3 Epistemological Delimitation: KDD vs. Data Mining vs. Data Science

Related concepts

Main connections

  • Linear algebra & matrices
  • Variance & standard deviation
  • Missing data & multiple imputation
  • Mean & expected value
  • Maximum likelihood estimation
  • Outliers & influence diagnostics

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

Comp(Aj)=1−∑i=1N𝟙{null}(xi,j)N
View LaTeX source\text{Comp}(A_j) = 1 - \frac{\sum_{i=1}^{N} \mathbb{1}_{\{null\}}(x_{i,j})}{N}