Outlier Detection Based on Robust Mahalanobis Distance and Its Application
نویسندگان
چکیده
منابع مشابه
On Robust Mahalanobis Distance Issued from Fast Mcd and Mvv
In modern activities such as banking, homeland security, information transportation, telecommunication, etc., people work with large and high dimension data sets. But, the higher the dimension the higher the probability that outliers will be present in the data sets. The ability to detect outliers in high dimension multivariate data sets is a challenging task. In this circumstance, robust estim...
متن کاملA Study on Distance-based Outlier Detection on Uncertain Data
Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. The uncertainty information in the data is useful and can be used to improve the quality o...
متن کاملRapid Distance-Based Outlier Detection via Sampling
Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data. We present an empirical comparison of various approaches to distance-based outlier detection across a large number of datasets. We report the surprising observation that a simple, sampling...
متن کاملDistance-based Outlier Detection in Data Streams
Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier de...
متن کاملNormalized Clustering Algorithm Based on Mahalanobis Distance
FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. The added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Clustering Algorithm Based on Mahalanobis distance by t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Open Journal of Statistics
سال: 2019
ISSN: 2161-718X,2161-7198
DOI: 10.4236/ojs.2019.91002