Cart 0
Data Mining for Co-location Patterns
Click to zoom

Share this book

Data Mining for Co-location Patterns : Principles and Applications

Book Details

Format Paperback / Softback
ISBN-10 0367688662
ISBN-13 9780367688660
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date May 27th, 2024
Print length 228 Pages
Weight 420 grams
Ksh 8,800.00
Werezi Extended Catalogue 0 in stock

Delivery Location

Delivery fee: Select location

Secure
Quality
Fast
This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining for a step-by-step understanding.
Co-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.

Get Data Mining for Co-location Patterns by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Taylor & Francis Ltd and it has pages.

Mind, Body, & Spirit

Shopping Cart

Africa largest book store

Sub Total:
Ebooks

Digital Library
Coming Soon

Our digital collection is currently being curated to ensure the best possible reading experience on Werezi. We'll be launching our Ebooks platform shortly.