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Context-Based Distance Learning for Categorical Data Clustering
[chapter]
2009
Lecture Notes in Computer Science
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In this paper, we propose a method to learn a context-based distance for categorical attributes. ...
Clustering data described by categorical attributes is a challenging task in data mining applications. ...
Periklis Andritsos who provided the implementation of LIMBO, and Elena Roglia for stimulating discussions. Ruggero G. Pensa is co-funded by Regione Piemonte. ...
doi:10.1007/978-3-642-03915-7_8
fatcat:mdcy5zasgnhhdahrfh4zclf4w4
Context-Based Geodesic Dissimilarity Measure for Clustering Categorical Data
2021
Applied Sciences
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This study proposes a new method to measure the dissimilarity between two categorical observations, called a context-based geodesic dissimilarity measure, for the categorical data clustering problem. ...
The dissimilarity or distance computation has been a manageable problem for continuous data because many numerical operations can be successfully applied. ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their constructive comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app11188416
fatcat:o6sk4phj3ffanfhhu6vz3dzq4e
An Approach of Context Ontology for Robust Face Recognition Against Illumination Variations
2007
2007 International Conference on Information and Communication Technology
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Context ontology is built using context acquisition, context learning and context categorization. ...
, learning, and recognition. ...
Context Categorization Cosine distance is a popular distance measure for comparing documents in the information retrieval literature. ...
doi:10.1109/icict.2007.375351
fatcat:2x6cd2zg4vfu7hqjgdbkxdkvre
From Context to Distance
2012
ACM Transactions on Knowledge Discovery from Data
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In this paper, we propose a framework to learn a context-based distance for categorical attributes. ...
Clustering data described by categorical attributes is a challenging task in data mining applications. ...
Periklis Andritsos who provided the implementation of LIM BO, and Elena Roglia for stimulating discussions. We want to thank Regione Piemonte which co-funds Ruggero G. Pensa. ...
doi:10.1145/2133360.2133361
fatcat:z2wdlwi3gbf7rgjelbew5aoh2m
A Review on Outlier Detection Approaches
2019
International Journal for Research in Applied Science and Engineering Technology
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It is difficult to define distance between two categorical attributes because the values are not ordered and hence the outlier detection strategy is different for numerical and categorical attributes. ...
And new strategy for outlier detection is proposed. ...
For categorical data system uses context-based distance learning. The distance is evaluated using attributes values distribution over data objects. ...
doi:10.22214/ijraset.2019.3345
fatcat:hc6vputvczgpzpxm37bz5a2ijm
Context Aware Clustering Using Glove and K-Means
2017
International Journal of Software Engineering & Applications
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Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t-SNE to develop a a context-aware ...
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. ...
Manning of Stanford University for their work on Global Vectors (GloVe) for Word Representations. Without their research our work would not have been possible. ...
doi:10.5121/ijsea.2017.8403
fatcat:3arcmsxdg5azhn44dyg24o2txm
ConDist: A Context-Driven Categorical Distance Measure
[chapter]
2015
Lecture Notes in Computer Science
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A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. ...
We compare our new distance measure to existing categorical distance measures and evaluate on different data sets from the UCI machine-learning repository. ...
This work is funded by the Bavarian Ministry for Economic affairs through the WISENT project (grant no. IUK 452/002) and by the DFG through the PoSTS II project (grant no. STR 1191/3-2). ...
doi:10.1007/978-3-319-23528-8_16
fatcat:izqbhcdshrg3rh7kmfkk7pr2xa
Innovative Teaching-Learning Process: Categorical Clustering Data
2020
Journal of Engineering Education Transformations
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The present study is intended to explore the categorical clustering data. ...
The results revealed that there is a statistically significant difference in categorical data clustering with reference to gender as well as managementImplications and suggestions for further research ...
TEACHING-LEARNING PROCESS:
CATEGORICAL CLUSTERING DATA
1.1
Objectives of the study
1. ...
doi:10.16920/jeet/2020/v33i0/150207
fatcat:hkvgovuof5gh5hiwutb7dx5e7u
Exploiting Mobile Ad Hoc Networking and Knowledge Generation to Achieve Ambient Intelligence
2012
Applied Computational Intelligence and Soft Computing
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EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodes' mobility context from social scenario traces and ubiquitously learn "friends" and "strangers" indirectly and anonymously ...
The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF) to observe and categorize relationships and activities in the user and application level and based on that ...
Therefore, we apply an unsupervised approach to learn context from data in a passive (nonintrusive) mode without a priory knowledge and focus our study on context-awareness for routing services. ...
doi:10.1155/2012/262936
fatcat:32r5hqd7yzaqtfb5uebglqglv4
Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning
2021
2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
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We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobilenetwork setting. ...
More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. ...
Results for reference method 1: Numerical aggregations / Euclidean distance
Fig. 3 . 3 Fig. 3. Results for reference method 2: Categorical aggregations / Jaccard distance
Fig. 4 . 4 Fig. 4. ...
doi:10.1109/wimob52687.2021.9606307
fatcat:so7zvj3zmnaklmtkrzmxabwh2a
A space-structure based dissimilarity measure for categorical data
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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For this reason, we propose a new distance metric for categorical data. ...
Therefore, determining a dissimilarity measure for categorical data is one of the most attractive and recent challenges in data mining problems. ...
project "Desarrollo de una metodología para la identificación de perfiles de los consumidores del servicio público utilizando técnicas de aprendizaje de máquina" (number 2-20-8) funded by Vice-Rectory for ...
doi:10.11591/ijece.v11i1.pp620-627
fatcat:3n65lttyjjhy3guszrxfszhece
A NOVEL CLASSIFICATION APPROACH OF TRAVEL REVIEW DATASET BASED ON ENTERTAINMENT
2019
Indian Journal of Computer Science and Engineering
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A possible solution is to adopt clustering techniques to limit the data to be considered for recommendation process. ...
In tourism context, based on social media interactions like reviews, forums, blogs, feedbacks, etc. travelers can be clustered to form different interest groups. ...
Based on the data under consideration appropriate distance measure can be chosen for clustering. ...
doi:10.21817/indjcse/2019/v10i3/191003012
fatcat:7plpac3aczdi7n3qi2s222ryk4
Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation
2021
Advances in Technology Innovation
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Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. ...
Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors, are applied to the land sales records to delineate ...
For instance, categorical data such as the color of products with each element being black, blue, and red cannot be clustered based on the distance between the three colors. ...
doi:10.46604/aiti.2021.8492
fatcat:isk4oah2hbe4pecqnoxk77encq
Symbolic Distance Measurements Based on Characteristic Subspaces
[chapter]
2003
Lecture Notes in Computer Science
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We introduce the subspace difference metric, a novel heterogeneous distance metric for calculating distances between points with both continuous and (unordered) categorical attributes. ...
Our approach is based on the computation and comparison of characteristic subspaces (i.e. contexts) for each of the symbols and can be viewed as a generalization of the well-known value difference metric ...
The Austrian Research Institute for Artificial Intelligence acknowledges basic financial support by the Austrian Federal Ministry for Education, Science, and Culture. ...
doi:10.1007/978-3-540-39804-2_29
fatcat:wzheh6vzkbealj5yjcwadetbum
Mining entity attribute synonyms via compact clustering
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
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In this work, we propose a novel compact clustering framework to jointly identify synonyms for a set of attribute values. ...
Extensive experiments across multiple domains demonstrate the effectiveness of our clustering framework for mining entity attribute synonyms. ...
By mining the this context, we are able to discover categorical patterns, which would otherwise be impossible had we looked for synonyms one attribute value at a time due to data sparseness. ...
doi:10.1145/2505515.2505608
dblp:conf/cikm/LiHZW13
fatcat:g67oqzuhofcn7ddgpadjezwusi
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