Discovering association rules based on image content

Ordóñez, Carlos; Omiecinski, Edward

doi:10.1109/adl.1999.777689

Cited by 94 publications

(52 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If all the k-1 object subsets of C are in (k-1)-object table 10) If C is already in the k-object table 11) Increment the count of C; 12)…”

Section: Viewpointminer Algorithmmentioning

confidence: 99%

“…Existing works adapted data mining algorithms such as association rule mining [10] [13], clustering [2] and classification techniques [3] [11] to generate patterns based on pixel level or object level features. While these approaches can discover hidden relationships among appearance features in t he images, they ignore the patterns relating to the spatial properties of objects in the images.…”

Section: Image Miningmentioning

confidence: 99%

See 1 more Smart Citation

Mining viewpoint patterns in image databases

Hsu

Dai

Lee

2003

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

The increasing number of image repositories has made image mining an important task because of its potential in discovering useful image patterns from a large set of images. In this paper, we introduce the notion of viewpoint patterns for image databases. Viewpoint patterns refer to patterns that capture the invariant relationships of one object from the point of view of another object. These patterns are unique and significant in images because the absolute positional information of objects for most images is not important, but rather, it i s the relative distance and orientation of the objects from each other that is meaningful. We design a scalable and efficient algorithm to discover such viewpoint patterns. Experiments results on various image sets demonstrate that viewpoint patterns are m eaningful and interesting to human users.

show abstract

“…If all the k-1 object subsets of C are in (k-1)-object table 10) If C is already in the k-object table 11) Increment the count of C; 12)…”

Section: Viewpointminer Algorithmmentioning

confidence: 99%

Section: Image Miningmentioning

confidence: 99%

Mining viewpoint patterns in image databases

Hsu

Dai

Lee

2003

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…From large collection of mammograms, image mining system automatically finds the meaningful information or knowledge. In [4] authors introduce mining concepts on image datasets to extract the knowledge. They focus on process of extracting the association rules from color images.…”

Section: Introductionmentioning

confidence: 99%

GLCM based Improved Mammogram Classification using Associative Classifier

Deshmukh¹,

Bhosle²

2017

IJIGSP

View full text Add to dashboard Cite

Abstract-Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported [1]. Nowadays early detection of breast cancer became very important. Mammogram -a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations.The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.

show abstract

“…The first direction involves domain-specific applications where the focus is to extract the most relevant image features into a form suitable for data mining [10,11]. The second direction involves general applications where the focus is to generate image patterns that maybe helpful in the understanding of the interaction between high-level human perceptions of images and low level image features [2,8,12]. Clustering medical images belongs to the first direction.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], localization of the visual features, their spatial relationships and their motion in time (for video) are presented. A discovering association rules algorithm based on image content from a simple image dataset is presented in [8]. [9] Research in image mining can be broadly classified into two main directions [3].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Domain Knowledge into Medical Image Mining

Pan¹

2012

Data Mining Applications in Engineering and Medicine

View full text Add to dashboard Cite

Discovering association rules based on image content

Cited by 94 publications

References 7 publications

Mining viewpoint patterns in image databases

Mining viewpoint patterns in image databases

GLCM based Improved Mammogram Classification using Associative Classifier

Incorporating Domain Knowledge into Medical Image Mining

Contact Info

Product

Resources

About