Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …
sets that can be considered correct and useful for further data mining algorithms. This paper …
Techniques for dealing with incomplete data: a tutorial and survey
Real-world applications of pattern recognition, or machine learning algorithms, often present
situations where the data are partly missing, corrupted by noise, or otherwise incomplete. In …
situations where the data are partly missing, corrupted by noise, or otherwise incomplete. In …
A survey on pre-processing techniques: Relevant issues in the context of environmental data mining
One of the important issues related with all types of data analysis, either statistical data
analysis, machine learning, data mining, data science or whatever form of data-driven …
analysis, machine learning, data mining, data science or whatever form of data-driven …
Dealing with missing values
S García, J Luengo, F Herrera, S García… - Data preprocessing in …, 2015 - Springer
In this chapter the reader is introduced to the approaches used in the literature to tackle the
presence of Missing Values (MVs). In real-life data, information is frequently lost in data …
presence of Missing Values (MVs). In real-life data, information is frequently lost in data …
Clustering educational data
The Internet and the advance of telecommunication technologies allow us to share and
manipulate information in nearly real time. This reality is determining the next generation of …
manipulate information in nearly real time. This reality is determining the next generation of …
Development of a patent roadmap through the Generative Topographic Mapping and Bass diffusion model
This paper aims to present a novel concept roadmap—the patent roadmap—and suggest an
advanced patent roadmapping process, based on the Generative Topographic Mapping …
advanced patent roadmapping process, based on the Generative Topographic Mapping …
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Missing data are a part of almost all research, and we all have to decide how to deal with it
from time to time. There are a number of alternative ways of dealing with missing data. The …
from time to time. There are a number of alternative ways of dealing with missing data. The …
Probabilistic self-organizing maps for continuous data
E López-Rubio - IEEE Transactions on Neural Networks, 2010 - ieeexplore.ieee.org
The original self-organizing feature map did not define any probability distribution on the
input space. However, the advantages of introducing probabilistic methodologies into self …
input space. However, the advantages of introducing probabilistic methodologies into self …
Advances in clustering and visualization of time series using GTM through time
Most of the existing research on multivariate time series concerns supervised forecasting
problems. In comparison, little research has been devoted to their exploration through …
problems. In comparison, little research has been devoted to their exploration through …
Generative adversarial learning for missing data imputation
X Wang, H Chen, J Zhang, J Fan - Neural Computing and Applications, 2024 - Springer
Missing data widely exist in industrial problems and lead to difficulties in further modeling
and analysis. Recently, a number of deep learning methods have been proposed for …
and analysis. Recently, a number of deep learning methods have been proposed for …