POD: A Parallel Outlier Detection Algorithm Using Weighted kNN
نویسندگان
چکیده
منابع مشابه
Spatial Weighted Outlier Detection
Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal valuable information from large spatial data sets. In many real applications, spatial objects can not be simply abstracted as isolated points. They have different boundary, size, volume, and location. These spatial properties affect the impact of a spatial...
متن کاملThe Spatial Outlier Mining Algorithm based on the KNN Graph
In order to solve the defect in the spatial outlier mining algorithm that the spatial objects may be affected by their surrounding abnormal neighbors, a Based K-Nearest Neighbor (BKNN) algorithm was proposed based on the working principle of KNN Graph, which could effectively identify the spatial outliers by using cutting edge strategies. The core idea of BKNN is to calculate the dissimilarity ...
متن کاملABC-based distance-weighted kNN algorithm
Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opin...
متن کاملParallel Corpus Refinement as an Outlier Detection Algorithm
Filtering noisy parallel corpora or removing mistranslations out of training sets can improve the quality of a statistical machine translation. Discriminative methods for filtering the corpora such as a maximum entropy model, need properly labeled training data, which are usually unavailable. Generating all possible sentence pairs (the Cartesian product) to generate labeled data, produces an im...
متن کاملOutlier Detection for Dynamic Data Streams Using Weighted K-means
This paper presents a new k-means type clustering algorithm that can calculate weights to the variables. This method is efficient for dynamic data streams in order to overcome the global optimum problems. The variable weights produced by the algorithm measures the importance of variable in clustering and can be used in variable selection in which the data items with similar properties are group...
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: IEEE Access
سال: 2021
ISSN: 2169-3536
DOI: 10.1109/access.2021.3085605