Segmentation of dynamic PET images using cluster analysis

Wong, Koon-Pong; Feng, Dan; Meikle, S.R.; Fulham, Michael

doi:10.1109/nssmic.2000.949251

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clustering has been utilized for various data mining tasks like data summation and classification. In many areas such as research and development [2], marketing [3], medicine [4], nuclear science [5], software engineering [6] and radar scanning [7] clustering techniques are used. Large scale research and development planning is identified by Mathieu and Gibson [2] using cluster analysis as a part of a decision support tool to participate and determine resource allocation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Analysis of Rough Set Categorical Clustering Techniques

2017

View full text Add to dashboard Cite

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Wong etal. [5] presented an approach for positron emission tomography (PET) that is used to segment tissues in a nuclear medical imaging. Radar signals are segmented in marine objects and scanning land by Haimov et al [7] using cluster analysis.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of Rough Set Categorical Clustering Techniques

2017

View full text Add to dashboard Cite

show abstract

“…Fuzzy C-means (FCM) and K-means algorithms [24], Iterative self-organizing data analysis technique algorithm [25] and unsupervised neural networks [26] are some of the popular unsupervised clustering algorithms. Instead of using a training dataset, these algorithms extract structural features from the classified points.…”

Section: Pattern Recognition-based Methodsmentioning

confidence: 99%