Measuring Interestingness – Perspectives on Anomaly Detection

We live in a data deluge. Our ability to gather, distribute, and store information has grown immensely over the past two decades. With this overabundance of data, the core knowledge discovery problem is no longer in the gathering of this data, but rather in the retrieving of relevant data efficiently. While the most common approach is to use rule interestingness to filter results of the association rule generation process, study of literature suggests that interestingness is difficult to define quantitatively and is best summarized as, “a record or pattern is interesting if it suggests a change in an established model.” In this paper we elaborate on the term interestingness, and the surrounding taxonomy of interestingness measures, anomalies, novelty and surprisingness. We review and summarize the current state of literature surrounding interestingness and associated approaches. Keywords: Interestingness, anomaly detection, rare-class mining, Interestingness measures, outliers, surprisingness, novelt

A Remote Sensing Image Processing Framework for Damage Assessment in a Forest Fire Scenario

In natural hazards management applications Earth Observation (EO) image processing methods are based on segmentation and classification. The result primary consists of thematic maps which are readily interpretable. We propose a complete EO image processing chain, which generates an end product with increased information content organized in thematic layers. The processing chain integrates four main components: image classification, identification of high anomaly areas relative to the entire scene context, spectral and texture change detection, and the integration of different information layers. The processing chain was tested in a fire management scenario, using a pair of Landsat5-TM images for the Pagami Creek forest fire which was active from August to October 2011