A Proximity Measure and a Clustering Method for Concept Extraction in an Ontology Building Perspective

2006 ◽  
pp. 697-706
Author(s):  
Guillaume Cleuziou ◽  
Sylvie Billot ◽  
Stanislas Lew ◽  
Lionel Martin ◽  
Christel Vrain
Keyword(s):  
Clustering Method ◽  
Proximity Measure ◽  
Concept Extraction ◽  
Ontology Building

Research of Conceptual Relation Extraction Based on Improved Hierarchical Clustering Method

2014 ◽  
Vol 8 (1) ◽  
pp. 355-360
Author(s):  
Caiyun Xie ◽  
Junyun Wu
Keyword(s):  
Relation Extraction ◽  
Main Task ◽  
Semantic Retrieval ◽  
Ontology Learning ◽  
Clustering Method ◽  
Concept Hierarchy ◽  
Concept Extraction ◽  
Relationship Extraction ◽  
Taxonomic Relation ◽  
Conceptual Relation

The main task of Ontology learning is concept extraction and conceptual relation extraction. This paper mainly studies the latter. Conceptual relation consists of taxonomic relation and non-taxonomic relation. It introduces hierarchy clustering method, and uses concept hierarchy clustering method which chooses different clustering standards in each hierarchy to obtain the taxonomic relation. It improves the accuracy of the relationship extraction. For extracting the nontaxonomic relation, this paper uses a extended association rule, this method can get concrete names of relation, and confirms the domain and range. In the end, the paper uses the introduced method of Ontology Learning to constructing a domain ontology in the law. And it completes the implementation of an Ontology-based semantic retrieval system. The final effect of this system application demonstrates that this Ontology learning method is efficient.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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