''Preface Contrasting is one of the most basic types of analysis. Contrasting based analysis is routinely employed, often subconsciously, by all types of people. People use contrasting to better understand the world around them and the challenging problems they want to solve. People use contrasting to accurately assess the desirability of important situations, and to help them better avoid potentially harmful situations and embrace potentially beneficial ones. Contrasting involves the comparison of one dataset against another. The datasets may represent data of different time periods, spatial locations, or classes, or they may represent data satisfying different conditions. Contrasting is often employed to compare cases with a desirable outcome against cases with an undesirable one, for example comparing the benign and diseased tissue classes of a cancer, or comparing students who graduate with university degrees against those who do not. Contrasting can identify patterns that capture changes and trends over time or space, or identify discriminative patterns that capture differences among contrasting classes or conditions. Traditional methods for contrasting multiple datasets were often very simple so that they could be performed by hand. For example, one could compare the respective feature means, compare the respective attribute-value distributions, or compare the respective probabilities of simple patterns, in the datasets being contrasted. However, the simplicity of such approaches has limitations, as it is difficult to use them to identify specific patterns that offer novel and actionable insights, and identify desirable sets of discriminative patterns for building accurate and explainable classifiers''-- Read more...
Content: Preliminaries and statistical contrast measures: Preliminaries / Guozhu Dong ; Statistical measures for contrast patterns / James Bailey --
Contrast mining algorithms: Mining emerging patterns using tree structures or tree based searches / James Bailey and Kotagiri Ramamohanarao ; Mining emerging patterns using zero-suppressed binary decision diagrams / James Bailey and Elsa Loekito ; Efficient direct mining of selective discriminative patterns for classification / Hong Cheng, Jaiwei Han, Xifeng Yan, and Philip S. Yu ; Mining emerging patterns from structured data / James Bailey ; Incremental maintenance of emerging patterns --
Generalized contrasts, emerging data cubes, and rough sets: More expressive contrast patterns and their mining / Lei Duan, Milton Garcia Borroto, and Guozhu Dong ; Emerging data cube representations for OLAP database mining / Sébastien Nedjar, Lotfi Lakhal, and Rosine Cicchetti ; Relation between jumping emerging patterns and rough set theory / Pawel Terlecki and Krzysztof Walczak --
Contrast mining for classification & clustering: Overview and analysis of contrast pattern based classification / Xiuzhen Zhang and Gouzhu Dong ; Using emerging patterns in outlier and rare-class prediction / Lijun Chen and Guozhu Dong ; Enhancing traditional classifiers using emerging patterns / Guozhu Dong and Kotagiri Ramamohanarao --
CPC: a contrast pattern based clustering algorithm / Neil Fore and Guozhu Dong. Contrast mining for bioinformatics and chemoinformatics: Emerging pattern based rules characterizing subtypes of leukemia / Jinyan Li and Limsoon Wong ; Discriminating gene transfer and microarray concordance analysis / Shihong Mao and Guozhu Dong ; Towards mining optimal emerging patterns amidst 1000s of genes / Shihong Mao and Guozhu Dong ; Emerging chemical patterns, theory and applications / Jens Auer, Martin Vogt, and Jürgen Bajorath ; Emerging patterns as structural alerts for computational toxicology / Bertrand Cuissart, Guillaume Poezevara, Bruno Crémilleux, Alban Lepailleur, and Ronan Bureau --
Contrast mining for special domains: Emerging patterns and classification for spatial and image data / Lukasz Kobyliński and Krzysztof Walczak --
Geospatial contrast mining with applications on labeled spatial data / Wei Ding, Thomasz F. Stepinski, and Josue Salazar ; Mining emerging patterns for activity recognition / Tao Gu, Zhanquing Wu, XianPing Tao, Hung Keng Pung, and Jian Lu ; Emerging pattern based prediction of heart diseases and powerline safety / Keun Ho Ryu, Dong Gyu Lee, and Minghao Piao --
Emerging pattern based crime spots analysis and rental price prediction / Naoki Katoh and Atsushi Takizawa --
Survey of other papers: Overview of results on contrast mining and applications / Guozhu Dong.
Abstract: ''Preface Contrasting is one of the most basic types of analysis. Contrasting based analysis is routinely employed, often subconsciously, by all types of people. People use contrasting to better understand the world around them and the challenging problems they want to solve. People use contrasting to accurately assess the desirability of important situations, and to help them better avoid potentially harmful situations and embrace potentially beneficial ones. Contrasting involves the comparison of one dataset against another. The datasets may represent data of different time periods, spatial locations, or classes, or they may represent data satisfying different conditions. Contrasting is often employed to compare cases with a desirable outcome against cases with an undesirable one, for example comparing the benign and diseased tissue classes of a cancer, or comparing students who graduate with university degrees against those who do not. Contrasting can identify patterns that capture changes and trends over time or space, or identify discriminative patterns that capture differences among contrasting classes or conditions. Traditional methods for contrasting multiple datasets were often very simple so that they could be performed by hand. For example, one could compare the respective feature means, compare the respective attribute-value distributions, or compare the respective probabilities of simple patterns, in the datasets being contrasted. However, the simplicity of such approaches has limitations, as it is difficult to use them to identify specific patterns that offer novel and actionable insights, and identify desirable sets of discriminative patterns for building accurate and explainable classifiers''
