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

Robust and cost-effective approach for discovering action rules

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework