The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Semi-supervised classification methods are suitable tools to tackle training sets with large
amounts of unlabeled data and a small quantity of labeled data. This problem has been …

摘要AdaBoost 是最优秀的Boosting 算法之一, 有着坚实的理论基础, 在实践中得到了很好的
推广和应用. 算法能够将比随机猜测略好的弱分类器提升为分类精度高的**分类器 …

Computers are now involved in many economic transactions and can capture data
associated with these transactions, which can then be manipulated and analyzed …

Abstract The k-Nearest Neighbors classifier is a simple yet effective widely renowned
method in data mining. The actual application of this model in the big data domain is not …

We present two modifications to the popular k-means clustering algorithm to address the
extreme requirements for latency, scalability, and sparsity encountered in user-facing web …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis
and has made great success in practical applications. AdaBoost can boost a weak learning …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …