57 research outputs found
Certainty of outlier and boundary points processing in data mining
Data certainty is one of the issues in the real-world applications which is
caused by unwanted noise in data. Recently, more attentions have been paid to
overcome this problem. We proposed a new method based on neutrosophic set (NS)
theory to detect boundary and outlier points as challenging points in
clustering methods. Generally, firstly, a certainty value is assigned to data
points based on the proposed definition in NS. Then, certainty set is presented
for the proposed cost function in NS domain by considering a set of main
clusters and noise cluster. After that, the proposed cost function is minimized
by gradient descent method. Data points are clustered based on their membership
degrees. Outlier points are assigned to noise cluster and boundary points are
assigned to main clusters with almost same membership degrees. To show the
effectiveness of the proposed method, two types of datasets including 3
datasets in Scatter type and 4 datasets in UCI type are used. Results
demonstrate that the proposed cost function handles boundary and outlier points
with more accurate membership degrees and outperforms existing state of the art
clustering methods.Comment: Conference Paper, 6 page
Borhan: A Novel System for Prioritized Default Logic
Prioritized Default Logic presents an optimal solution for addressing
real-world problems characterized by incomplete information and the need to
establish preferences among diverse scenarios. Although it has reached great
success in the theoretical aspect, its practical implementation has received
less attention. In this article, we introduce Borhan, a system designed and
created for prioritized default logic reasoning. To create an effective system,
we have refined existing default logic definitions, including the extension
concept, and introduced novel concepts. In addition to its theoretical merits,
Borhan proves its practical utility by efficiently addressing a range of
prioritized default logic problems. In addition, one of the advantages of our
system is its ability to both store and report the explanation path for any
inferred triple, enhancing transparency and interpretability. Borhan is offered
as an open-source system, implemented in Python, and even offers a simplified
Java version as a plugin for the Protege ontology editor. Borhan thus
represents a significant step forward in bridging the gap between the
theoretical foundations of default logic and its real-world applications
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