17,237 research outputs found
Efficiency Analysis of Hybrid Fuzzy C-Means Clustering Algorithms and their Application to Compute the Severity of Disease in Plant Leaves
Data clustering has a wide range of application varying from medical image analysis, social network analysis, market segmentation, search engines, recommender systems and image processing. A clustering algorithm should be fast as well accurate. Some applications give priority to the speed of the clustering algorithms while some emphasize more on the accuracy rather than speed. A number of clustering algorithms have been proposed in the literature. Some of these include Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM). In this paper, we compare the accuracy and execution time of the fuzzy based clustering algorithms. The clustering algorithms are applied on an image dataset and their running time as well as accuracy is compared by varying the number of clusters. Our results show that there is a clear trade-off between execution time and accuracy of these clustering algorithms. Also, we apply these algorithms on two different diseased leaf images and compute the severity of the disease of the leaves
Autonomous clustering using rough set theory
This paper proposes a clustering technique that minimises the need for subjective
human intervention and is based on elements of rough set theory. The proposed algorithm is
unified in its approach to clustering and makes use of both local and global data properties to
obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and
results from three data sets of single and mixed attribute types are used to illustrate the
technique and establish its efficiency
Taming Wild High Dimensional Text Data with a Fuzzy Lash
The bag of words (BOW) represents a corpus in a matrix whose elements are the
frequency of words. However, each row in the matrix is a very high-dimensional
sparse vector. Dimension reduction (DR) is a popular method to address sparsity
and high-dimensionality issues. Among different strategies to develop DR
method, Unsupervised Feature Transformation (UFT) is a popular strategy to map
all words on a new basis to represent BOW. The recent increase of text data and
its challenges imply that DR area still needs new perspectives. Although a wide
range of methods based on the UFT strategy has been developed, the fuzzy
approach has not been considered for DR based on this strategy. This research
investigates the application of fuzzy clustering as a DR method based on the
UFT strategy to collapse BOW matrix to provide a lower-dimensional
representation of documents instead of the words in a corpus. The quantitative
evaluation shows that fuzzy clustering produces superior performance and
features to Principal Components Analysis (PCA) and Singular Value
Decomposition (SVD), two popular DR methods based on the UFT strategy
An intelligent recommendation system framework for student relationship management
In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification
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