Large dataset summarization with automatic parameter optimization and parallel processing for local outlier detection

2018 ◽  
Vol 30 (23) ◽  
pp. e4466 ◽  
Author(s):  
Zhaoyu Shou ◽  
Simin Li
Keyword(s):  
Parallel Processing ◽  
Outlier Detection ◽  
Parameter Optimization ◽  
Large Dataset ◽  
Local Outlier

scholarly journals SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

2021 ◽  
pp. 107256
Author(s):  
Sayyed Ahmad Naghavi Nozad ◽  
Maryam Amir Haeri ◽  
Gianluigi Folino
Keyword(s):  
Outlier Detection ◽  
Density Based Clustering ◽  
Massive Scale ◽  
Local Outlier

Scalable KDE-based top-n local outlier detection over large-scale data streams

2020 ◽  
Vol 204 ◽  
pp. 106186 ◽  
Author(s):  
Fang Liu ◽  
Yanwei Yu ◽  
Peng Song ◽  
Yangyang Fan ◽  
Xiangrong Tong
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data ◽  
Local Outlier

Optimal KD-Partitioning for the Local Outlier Detection in Geo-Social Points

2017 ◽  
pp. 104-112
Author(s):  
Teerawat Kumrai ◽  
Kyoung-Sook Kim ◽  
Mianxiong Dong ◽  
Hirotaka Ogawa
Keyword(s):  
Outlier Detection ◽  
Local Outlier

An efficient local outlier detection optimized by rough clustering

2021 ◽  
pp. 1-12
Author(s):  
Chunyan She ◽  
Shaohua Zeng
Keyword(s):  
Outlier Detection ◽  
False Positive Rate ◽  
Time Cost ◽  
Local Distribution ◽  
Running Speed ◽  
Local Outlier Factor ◽  
Real World Applications ◽  
Positive Rate ◽  
Nearest Neighborhood ◽  
Local Outlier

Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others.

Local outlier detection based on information entropy weighting

2019 ◽  
Vol 30 (4) ◽  
pp. 207
Author(s):  
Lina Wang ◽  
Chao Feng ◽  
Yongjun Ren ◽  
Jinyue Xia
Keyword(s):  
Outlier Detection ◽  
Information Entropy ◽  
Local Outlier
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