64,927 research outputs found
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases
In recent years, discovery of association rules among itemsets in a large
database has been described as an important database-mining problem. The
problem of discovering association rules has received considerable research
attention and several algorithms for mining frequent itemsets have been
developed. Many algorithms have been proposed to discover rules at single
concept level. However, mining association rules at multiple concept levels may
lead to the discovery of more specific and concrete knowledge from data. The
discovery of multiple level association rules is very much useful in many
applications. In most of the studies for multiple level association rule
mining, the database is scanned repeatedly which affects the efficiency of
mining process. In this research paper, a new method for discovering multilevel
association rules is proposed. It is based on FP-tree structure and uses
cooccurrence frequent item tree to find frequent items in multilevel concept
hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
An information-driven framework for image mining
[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or
image sequence can be processed to identify high-level spatial objects and relationships. To meet
this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval
techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful
patterns/knowledge from each level
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