Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation
To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to...
Main Authors: | Zne-Jung Lee, Chou-Yuan Lee, Li-Yun Chang, Natsuki Sano |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/13/9/1557 |
Similar Items
-
Building efficient fuzzy regression trees for large scale and high dimensional problems
by: Javier Cózar, et al.
Published: (2018-12-01) -
Deconvolute individual genomes from metagenome sequences through short read clustering
by: Kexue Li, et al.
Published: (2020-04-01) -
DENCAST: distributed density-based clustering for multi-target regression
by: Roberto Corizzo, et al.
Published: (2019-06-01) -
Scaling associative classification for very large datasets
by: Luca Venturini, et al.
Published: (2017-12-01) -
A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data
by: Andreas Kanavos, et al.
Published: (2021-08-01)