The possibility of missing or incomplete data is often ignored when describing statistical or
machine learning methods, but as it is a common problem in practice, it is relevant to …

Missing values are a common occurrence in a number of real world databases, and
statistical methods have been developed to deal with this problem, referred to as missing …

We provide the first coreset for clustering points in $\mathbb {R}^ d $ that have multiple
missing values (coordinates). Previous coreset constructions only allow one missing …

Many real-world clustering problems are plagued by incomplete data characterized by
missing or absent features for some or all of the data instances. Traditional clustering …

In data mining field, experimental data sets are often incomplete due to the imperfect nature
of real world situations. However, the incompleteness of data sets generally leads to biased …

The distribution of distances between points in a high-dimensional data set tends to look
quite different from the distribution of the distances in a low-dimensional data set …

Increasing pressure on tropical forests is continually highlighting the need to find new
solutions that mitigate the impact of human populations on biodiversity. However …

The k‐means algorithm is arguably the most popular nonparametric clustering method but
cannot generally be applied to datasets with incomplete records. The usual practice then is …

Power transformers are an important part of the power system. Accurate monitoring of its
operating status is particularly important for the normal and stable operation of the entire …

Density estimation is one of the most frequently used data analytics techniques. A major
challenge of real-world datasets is missing values, originating eg from sampling errors or …