Learning what matters - Sampling interesting patterns

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.Comment: PAKDD 2017, extended versio

Interactive data analysis and its applications on multi-structured datasets

Ph.DDOCTOR OF PHILOSOPH

Label Ranking with Probabilistic Models

Diese Arbeit konzentriert sich auf eine spezielle Prognoseform, das sogenannte Label Ranking. Auf den Punkt gebracht, kann Label Ranking als eine Erweiterung des herkömmlichen Klassifizierungproblems betrachtet werden. Bei einer Anfrage (z. B. durch einen Kunden) und einem vordefinierten Set von Kandidaten Labels (zB AUDI, BMW, VW), wird ein einzelnes Label (zB BMW) zur Vorhersage in der Klassifizierung benötigt, während ein komplettes Ranking aller Label (zB BMW> VW> Audi) für das Label Ranking erforderlich ist. Da Vorhersagen dieser Art, bei vielen Problemen der realen Welt nützlich sind, können Label Ranking-Methoden in mehreren Anwendungen, darunter Information Retrieval, Kundenwunsch Lernen und E-Commerce eingesetzt werden. Die vorliegende Arbeit stellt eine Auswahl an Methoden für Label-Ranking vor, die Maschinelles Lernen mit statistischen Bewertungsmodellen kombiniert. Wir konzentrieren wir uns auf zwei statistische Ranking-Modelle, das Mallows- und das Plackett-Luce-Modell und zwei Techniken des maschinellen Lernens, das Beispielbasierte Lernen und das Verallgemeinernde Lineare Modell

High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets

멸종위기 동물 보호 캠페인을 위한 시각 커뮤니케이션의 차별화 연구

학위논문(석사)--서울대학교 대학원 :미술대학 디자인학부 디자인전공,2019. 8. 김경선.Endangered animal-related conservation campaigns have a long history since the later 19th century when natural resources were severely over-exploited. Through ages, endangered animal-related campaigns have gone through the era of legislation, resource management, environmentalism, and modern landscape-scale conservation. Since the situation of endangered species is getting worse, which the extinction rate is 1000 times higher than the background rate, the issue of wildlife conservation is becoming more and more significant. As a result, the popularity of this issue increased in present societies, accompanied by the investment increase of nature documentaries and related campaigns. However, the visual communication strategy of these campaigns remains unchanged for a long time, which is based on violence and conflict. Through this research, it can be known that more than 80% of the endangered animal-related campaigns preferred to display conflicting human-nature relationships, by showing violent, cruel, or bloody scenes to the audience. Even more, scientifically confusing or incorrect information, even thoughts of radical environmentalism were concealed in some of the campaign designs. By reviewing the history of western societies, the philosophy of binary opposition has a significant contribution to the current design preference, profoundly influencing on the public. Recent research of psychology also gives evidence that using horror and terror is a beneficial strategy to arouse the publics attention. To avoid its increasingly apparent disadvantages, and to adapt to the changing situation of conservation education, a differentiated approach must be made. Through the discussion of the features, the motivation, and the influence of the current violence-conflict-based strategy, combining traditional oriental philosophy and art is an ideal option of offering a different design proposal for the endangered animal-related campaign. By utilizing the thoughts of Confucianism, a more harmonious relationship between human and nature will be created in the final project, using symbolic visual elements to build a bridge linking human and endangered species. Also, positive visual elements and nudges will be utilized in the final project to encourage the audience to act more positively and actively.19 세기부터 동물 자원을 과도하게 이용하게 되어 이러한 상황을 개선하기 위하여 멸종 위기 동물과 관련된 캠페인이 시작되었다. 시대 발전을 따라 캠페인 주제는 입법에서 자원 관리, 환경 보호 주의를 지나 지금 현대 보존생학 중의 자연 경관 규모의 보존 사상까지 변하였다. 하지만 현재 멸종 위기 동물의 생존 상황이 점차 악화되어 있다. 연구에 의하면 현재 동물의 멸종 속도가 자연 멸종 속도보다 1000 배 높다고 한다. 이어서 멸종 위기 동물을 보호하는 문제도 점차 중요해지고 대중에서도 더 많은 인기를 끌고 있다. 이러한 추세를 반영하여 자연 다큐멘터리와 환경 보호와 관련된 캠페인에 대한 투자도 증가하고 있다. 그러나 멸종 위기 동물의 생존 상황과 실제 보호 사업이 지속적으로 발전하고 있는 반면에 캠페인의 시각 커뮤니케이션 전략이 오랫동안 변하지 않았다. 이 연구를 통하여 약 80% 의 캠페인이 폭력과 충돌을 기반으로 대항적인 인간 - 자연 관계를 전시하는 것을 알 수 있다. 이러한 작품들은 주요 잔인하고 고통스러운 장면을 관객에게 제시한다. 그리고 일부러 충돌을 만들기 위하여 혼란스러운 정보와 잘못된 정보, 심지어 과격한 환경 보호 주의가 가끔 디자인 작품에담게 되었다. 서양 사회의 역사와 발전을 회고하고 이항대립 사상이 대중의 의식과 긴밀하게 결합되었고 현재의 캠페인 디자인 전략에도 결정적인 영향을 주는 사실을 알게 되었다. 그리고 최근의 심리학 연구에 의하면 공포의 감정을 이용하여 정보 전파 속도도 빠르며 전파 효과도 크다고 한다. 그래서 폭력과 충돌을 중심으로 세운 캠페인 디자인 전략이 자연스럽게 선호되었다. 이 전략의 단점과 역효과도 피하고 현재 보존생물학의 발전도 잘 반영하기 위하여 또 다른 차별화된 시도가 필요하다. 이 연구에서는 폭력과 충돌을 중심으로 세운 전략의 특징과 동기, 영향 등을 분석한 후에 동양적인 철학 사상과 전통 미술을 결합하여 멸종 위기 동물을 홍보하고 보호하기 위한 차별화된 시각 커뮤니케이션 방안을 제안할 예정이다. 최종 프로젝트에서 전통 유가 사상을 참고할 예정인데 상징적인 시각 요소를 사용하여 멸종 위기 동물과 인간의 이미지를 연결할 것이다. 이러한 방식을 통하여 조화로운 인간 - 자연 관계를 맺을 수 있다. 그리고 최종 작품에서 긍정적인시각 요소와 넛지도 이용하고 관객들이 더 적극적으로 행동할 수 있게 유도할 예정이다.1. Introduction 1 2. The Necessity of Diversified Endangered Animal-Related Campaigns 3 2.1 The Current Situation of Endangered Animals 3 2.2 A Brief History of Wildlife Conservation 4 3. Analysis of the Current Design Preference of Endangered Animal-Related Campaigns 8 3.1. General Analysis of Visual Element Usage 8 3.2. The Utilization of Violence 14 3.3. The Utilization of Negative Human Images 27 3.4. Questionable Information Due to Rote Scene Generating 30 3.5. Conclusion 36 4. The Motivation and the Influence of the Current Design Preference 37 4.1. The Motivation on Strategic Planning Level 37 4.2. The Motivation on Problem Resolving Level 39 4.3. The Influence of Violence-Conflict-Based Strategy 41 5. Differentiated Approach in Communication of Endangered Animal-Related Campaigns 44 5.1. Using Positive Visual Elements 44 5.2. Applying Oriental Philosophy 48 5.3. The Confluent Design Methodology 50 5.4. Experimental Approaches 52 5.5. The Final Project 56 6. Discussions and Future Works 69 6.1. The Results and Discussions 69 6.2. Future Works 72 6.3. Conclusion 72 Bibliography 73 Appendix I. Samples of Current Design Preference 79 Appendix II. Final Design of the Posters 98 Appendix III. Localization of the Posters 103 Visual Sources Citations 108 Abstract in Korean 115 Acknowledgment 117Maste

Happiness Maximizing Sets under Group Fairness Constraints (Technical Report)

Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and admission, it is crucial to ensure that the HMS can fairly represent different groups of candidates without bias and discrimination. However, although the HMS problem was extensively studied in the database community, existing algorithms do not take group fairness into account and may provide solutions that under-represent some groups. In this paper, we propose and investigate a fair variant of HMS (FairHMS) that not only maximizes the minimum happiness ratio but also guarantees that the number of tuples chosen from each group falls within predefined lower and upper bounds. Similar to the vanilla HMS problem, we show that FairHMS is NP-hard in three and higher dimensions. Therefore, we first propose an exact interval cover-based algorithm called IntCov for FairHMS on two-dimensional databases. Then, we propose a bicriteria approximation algorithm called BiGreedy for FairHMS on multi-dimensional databases by transforming it into a submodular maximization problem under a matroid constraint. We also design an adaptive sampling strategy to improve the practical efficiency of BiGreedy. Extensive experiments on real-world and synthetic datasets confirm the efficacy and efficiency of our proposal.Comment: Technical report, a shorter version to appear in PVLDB 16(2