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作者： Owens, Clifford Conley Virginia Tech | University

As the world swims deeper into a deluge of data, binary datasets relating objects to properties can be found in many different fields. Such datasets abound in practically any area of interest, including biology, politics, entertainment, and education. This explosion calls for the definition of new types of patterns in binary data, as well as algorithms to find efficiently find these patterns. In this work, we introduce truth tables as a new class of patterns to be mined in binary datasets. Truth tables represent a subset of properties which exhibit maximal variability (and hence, suggest independence) in occurrence patterns over the underlying objects. Unlike other measures of independence, truth tables possess anti-monotone features that can be exploited in order to mine them effectively. We present a level-wise algorithm that takes advantage of these features, showing results on real and synthetic data. These results demonstrate the scalability of our algorithm. We also introduce new methods of mining straddling biclusters. Biclusters relate subsets of objects to subsets of properties they share within a single dataset. Straddling biclusters extend biclusters by relating a subset of objects to subsets of properties they share in two datasets. We present two levelwise algorithms, named UnionMiner and TwoMiner, which discover straddling biclusters efficiently by treating multiple datasets as a single dataset. We show results on real and synthetic data, and explore the advantages and limitations of each algorithm. We develop guidelines which suggest which of these algorithms is likely to perform better based on features of the datasets.

关键词： data mining binary datasets Thesis

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Exploring the Learnability of Numeric datasets

Exploring the Learnability of Numeric Datasets

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作者： Lin, Di Louisiana State University

学位级别：博士

When doing classification, it has often been observed that datasets may exhibit different levels of difficulty with respect to how accurately they can be classified. That is, there are some datasets which can be classified very accurately by many classification algorithms, and there also exist some other datasets that no classifier can classify them with high accuracy. Based on this observation, we try to address the following problems: a)what are the factors that make a dataset easy or difficult to be accurately classified b) how to use such factors to predict the difficulties of unclassified datasets and c) how to use such factors to improve classification. It turns out that the monotonic features of the datasets, along with some other closely related structural properties, play an important role in determining how difficult datasets can be accurately classified. More importantly, datasets which are comprised of highly monotonic data, can usually be classified more accurately than datasets with low monotonically distributed data. By further exploring these monotonicity based properties, we observed that datasets can always be decomposed into a family of subsets while each of them is highly monotonic locally. Moreover, it is proposed in this dissertation a methodology to use the classification models inferred from the smaller but highly monotonic subsets to construct a highly accurate classification model for the original dataset. Two groups of experiments were implemented in this dissertation. The first group of experiments were performed to discover the relationships between the data difficulty and data monotonic properties, and represent such relationships in regression models. Such models were later used to predict the classification difficulty of unclassified datasets. It seems that in more than 95% of the predictions, the deviations between the predicted value and the real difficulty are smaller than 2.4%. The second group of experiments focused on the perfor

关键词： Data mining classification models monotonic properties binary datasets monotone function monotonic characteristics

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binary Inscription Character Inpainting Based on Improved Context Encoders

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IEEE ACCESS 2023年 11卷 55834-55843页

作者： Xing, Chen Ren, Zhiyuan Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Shandong Peoples R China

Due to the limitations of textual datasets, there are currently few studies on character inpainting. Second, unlike image inpainting, character inpainting cannot replicate the results of image inpainting, and some methods are even inaccessible. Finally, the extraction of character features in the existing model is insufficient, resulting in the model being unable to reconstruct characters based on complete and accurate character features. In this paper, we first threshold the obtained inscription dataset to obtain a visually better-binarized inscription dataset. Second, we improve the Context encoders to design BCEs(binary Context Encoders) and add dilated convolution to learn the structural features of the character. It has been experimentally proven that BCEs is not only slightly better than methods in the same field, but can also restore real-life inscription characters with missing strokes.

关键词： Inscription character inpainting binary datasets context encoders dilated convolution

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A New Density Based Clustering Algorithm for binary Data Sets

A New Density Based Clustering Algorithm for Binary Data Set...

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International Conference on High Performance Computing and Applications (ICHPCA)

作者： Nanda, Satyasai Jagannath Raman, Rahul Vijay, Shubham Bhardwaj, Anil Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur Rajasthan India Shri Mata Vaishno Devi Univ Sch Elect & Commun Engn Katra India

ISBN: (纸本)9781479959587

binary Data clustering finds tremendous applications in fault analysis of machineries, document classification, image retrievals and analysis, medical diagnosis of diseases etc. Accurate clustering of binary databases provides numerous help to develop accurate designs of above systems. In this manuscript a density based clustering algorithm is proposed to effectively cluster binary datasets. The proposed algorithm automatically determines the number of clusters based upon the density of data present in a region. The number of clusters evolve during the clustering process due to merging of several smaller clusters. Simulation studies were carried out on two synthetic datasets and it is observed that the proposed algorithm can effectively clusters both correlated and random binary datasets.

关键词： Density based clustering DBSCAN binary datasets Similarity Measure

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