Ontotext

MARCH 8, 2023

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Increased awareness of and ability to leverage customer connections within these companies, helps foster positive customer relationships.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). This is what we term as ‘ recommender systems ’ which is now being implemented to boost sales by recommending products to frequent customers based on their previous purchase activities.

Data mining 77

Data mining KDD Data Science Forecasting 77

FineReport

JULY 16, 2021

It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. If the company has reached a high degree of informatization, the success rate of importing the BI system will definitely be greatly improved. Free Download.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

The Unofficial Google Data Science Blog

JANUARY 14, 2016

To understand this better we need a few definitions. But if a small fraction of user sessions have any purchase at all, then the coefficient of variation for the metric (sale price per session) will necessarily be even larger than that of the binary event (sessions with a sale). known, equal variances).

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

AUGUST 31, 2023

Knowledge graphs are large networks of entities representing real-world objects, like people and organizations, and abstract concepts, like professions and topics, and their semantic relations and attributes. An ontology enriches data within a knowledge graph with context and meaning that humans and computers can interpret.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Doing so makes it easier to study the effects of an intervention, say, a new marketing campaign, on the sales of a product. Another way to build a classifier for variance reduction is to address the rare event problem directly — what if we could predict a subset of instances in which the event of interest will definitely not occur?

Experimentation 40

Experimentation Metrics Statistics Measurement 40