scholarly journals Automatic Categorization of Human-Coded and Evolved CoreWar Warriors

2007 ◽  
pp. 589-596
Author(s):  
Nenad Tomašev ◽  
Doni Pracner ◽  
Miloš Radovanović ◽  
Mirjana Ivanović
Keyword(s):  
Automatic Categorization

scholarly journals A Similarity Measurement with Entropy-Based Weighting for Clustering Mixed Numerical and Categorical Datasets

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 184
Author(s):  
Xia Que ◽  
Siyuan Jiang ◽  
Jiaoyun Yang ◽  
Ning An
Keyword(s):  
Categorical Data ◽  
Clustering Algorithm ◽  
Numerical Data ◽  
Similarity Measurement ◽  
Amount Of Information ◽  
Automatic Categorization ◽  
Categorical Attributes ◽  
Weighting Strategy

Many mixed datasets with both numerical and categorical attributes have been collected in various fields, including medicine, biology, etc. Designing appropriate similarity measurements plays an important role in clustering these datasets. Many traditional measurements treat various attributes equally when measuring the similarity. However, different attributes may contribute differently as the amount of information they contained could vary a lot. In this paper, we propose a similarity measurement with entropy-based weighting for clustering mixed datasets. The numerical data are first transformed into categorical data by an automatic categorization technique. Then, an entropy-based weighting strategy is applied to denote the different importances of various attributes. We incorporate the proposed measurement into an iterative clustering algorithm, and extensive experiments show that this algorithm outperforms OCIL and K-Prototype methods with 2.13% and 4.28% improvements, respectively, in terms of accuracy on six mixed datasets from UCI.

scholarly journals A Partitioning Based Algorithm to Fuzzy Tricluster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Liu ◽  
Tengfei Yang ◽  
Lili Fu
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Membership Functions ◽  
Automatic Categorization ◽  
Dimensional Cube ◽  
Data Collections ◽  
Multiple Clusters

Fuzzy clustering allows an object to exist in multiple clusters and represents the affiliation of objects to clusters by memberships. It is extended to fuzzy coclustering by assigning both objects and features membership functions. In this paper we propose a new fuzzy triclustering (FTC) algorithm for automatic categorization of three-dimensional data collections. FTC specifies membership function for each dimension and is able to generate fuzzy clusters simultaneously on three dimensions. Thus FTC divides a three-dimensional cube into many little blocks which should be triclusters with strong coherent bonding among its members. The experimental studies onMovieLensdemonstrate the strength of FTC in terms of accuracy compared to some recent popular fuzzy clustering and coclustering approaches.

A COMPARATIVE EVALUATION OF FEATURES FOR MEDICAL IMAGE MODALITY CLASSIFICATION

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Sameer Ahmad Khan ◽  
Suet Peng Yong ◽  
Uzair Iqbal Janjua
Keyword(s):  
Comparative Evaluation ◽  
Medical Image ◽  
Medical Images ◽  
Image Categorization ◽  
Discriminatory Ability ◽  
Automatic Categorization ◽  
Image Characteristic ◽  
The Stability ◽  
Content Information ◽  
Image Modality

Medical images are increasing at an alarming rate. This increasing number of images affects the interpreting capacity of radiologists. In order to reduce the burden of radiologists, automatic categorization of medical images based on modality is the need of the hour. Because image modality is an important and fundamental image characteristic. The important factor in the automatic medical image categorization based on modality are the features used for categorization purpose, because nice treatment on these subtleties can lead to good results. Many descriptors have been proposed in the literature for medical image categorization. It is unclear which descriptor encodes the content information efficiently. The descriptors that are calculated from these medical images should be descriptive, distinctive and robust to various transformations. The stability of these descriptors are evaluated under various transformations and are then analyzed for their discriminatory ability for the task of classification. In this study the criteria of transformations, repeatability, matching and classification accuracy on the basis of precision recall is used to evaluate the performance of these descriptors. The experimental results illustrates that among global descriptors local features patches histogram and among local descriptors SIFT encodes the content information quite efficiently.

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