Yes / Real-world datasets are increasingly heterogeneous, showing a mixture of numerical, categorical and other feature types. The main challenge for mining heterogeneous datasets is how to deal with heterogeneity present in the dataset records. Although some existing classifiers (such as decision trees) can handle heterogeneous data in specific circumstances, the performance of such models may be still improved, because heterogeneity involves specific adjustments to similarity measurements and calculations. Moreover, heterogeneous data is still treated inconsistently and in ad-hoc manner. In this paper, we study the problem of heterogeneous data classification: our purpose is to use heterogeneity as a positive feature of the data classification effort by using consistently the similarity between data objects. We address the heterogeneity issue by studying the impact of mixing data types in the calculation of data objects’ similarity. To reach our goal, we propose an algorithm to divide the initial data records based on pairwise similarity for classification subtasks with the aim to increase the quality of the data subsets and apply specialized classifier models on them. The performance of the proposed approach is evaluated on 10 publicly available heterogeneous data sets. The results show that the models achieve better performance for heterogeneous datasets when using the proposed similarity process.

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The work is based on the algorithm SETTER (Secondary Structure Tertiary Structure-based Similarity Algorithm), which is designed to compare the 3D structures of RNA. SETTER in the original version can only compare pairs of RNA, however many applications require real similarity comparison between a set of RNA structures. The main idea used in MultiSETTER is a well-known approach used for multiple sequence alignment, which is based on the Neigbour-Joining method - a method for calculation of the taxonomic tree from distances between taxa - and on pairwise alignment. At each step the closest pair is aligned according to the taxonomic tree. To achieve good results, it was necessary to invent a method that creates a fictive average RNA structure by merging two RNAs that shares the structural characteristics of both RNA.

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Bibliography: p. 209-233. Over the last 50 years, psychologists have developed a range of frameworks for similarity modelling, along with a large number of numerical techniques for extracting mental representations from empirical data. This thesis is concerned with the psychological theories used to account for similarity judgements, as well as the mathematical and statistical issues that surround the numerical problem of finding appropriate representations. It discusses, evaluates, and further develops three widely-adopted approaches to similarity modelling: spatial, featural and tree representation.

Bibliography: p. 209-233. / xi, 233 p. : ill. (some col.) ; 29 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Over the last 50 years, psychologists have developed a range of frameworks for similarity modelling, along with a large number of numerical techniques for extracting mental representations from empirical data. This thesis is concerned with the psychological theories used to account for similarity judgements, as well as the mathematical and statistical issues that surround the numerical problem of finding appropriate representations. It discusses, evaluates, and further develops three widely-adopted approaches to similarity modelling: spatial, featural and tree representation. / Thesis (Ph.D.)--University of Adelaide, Dept. of Psychology, 2003?

We propose a novel approach for detecting visual similarity between two web pages. The proposed approach applies Gestalt theory and considers a webpage as a single indivisible entity. The concept of supersignals, as a realization of Gestalt principles, supports our contention that web pages must be treated as indivisible entities. We objectify, and directly compare, these indivisible supersignals using algorithmic complexity theory. We apply our new approach to the domain of anti-Phishing technologies, which at once gives us both a reasonable ground truth for the concept of “visually similar,” and a high-value application of our proposed approach. Phishing attacks involve sophisticated, fraudulent websites that are realistic enough to fool a significant number of victims into providing their account credentials. There is a constant tug-of-war between anti-Phishing researchers who create new schemes to detect Phishing scams, and Phishers who create countermeasures. Our approach to Phishing detection is based on one major signature of Phishing webpage which can not be easily changed by those con artists –Visual Similarity. The only way to fool this significant characteristic appears to be to make a visually dissimilar Phishing webpage, which also reduces the successful rate of the Phishing scams or their criminal profits dramatically. For this reason, our application appears to be quite robust against a variety of common countermeasures Phishers have employed. To verify the practicality of our proposed method, we perform a large-scale, real-world case study, based on “live” Phish captured from the Internet. Compression algorithms (as a practical operational realization of algorithmic complexity theory) are a critical component of our approach. Out of the vast number of compression techniques in the literature, we must determine which compression technique is best suited for our visual similarity problem. We therefore perform a comparison of nine compressors (including both 1-dimensional string compressors and 2-dimensional image compressors). We finally determine that the LZMA algorithm performs best for our problem. With this determination made, we test the LZMA-based similarity technique in a realistic anti-Phishing scenario. We construct a whitelist of protected sites, and compare the performance of our similarity technique when presented with a) some of the most popular legitimate sites, and b) live Phishing sites targeting the protected sites. We found that the accuracy of our technique is extremely high in this test; the true positive and false positive rates reached 100% and 0.8%, respectively. We finally undertake a more detailed investigation of the LZMA compression technique. Other authors have argued that compression techniques map objects to an implicit feature space consisting of the dictionary elements generated by the compressor. In testing this possibility on live Phishing data, we found that derived variables computed directly from the dictionary elements were indeed excellent predictors. In fact, by taking advantage of the specific characteristic of dictionary compression algorithm, we slightly improve on our accuracy when using a modified/refined LZMA algorithm for our already perfect NCD classification application. / Software Engineering and Intelligent Systems

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