Adapting K-means algorithm for discovering clusters in subspaces

Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters implicitly when measuring the similarities between points. We use the new similarity measure to improve traditional k-means algorithm for discovering clusters in subspaces. By clustering with low-dimensional minimal subspace distance first, the clusters in low-dimensional subspaces are detected. Then by gradually increasing the dimension of minimal subspace distance, the clusters get refined in higher dimensional subspaces. Our experiments on both synthetic data and real data show the effectiveness of the proposed similarity measure and algorithm. © Springer-Verlag Berlin Heidelberg 2006

Correlation Clustering

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. The core step of the KDD process is the application of a Data Mining algorithm in order to produce a particular enumeration of patterns and relationships in large databases. Clustering is one of the major data mining techniques and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized. This can serve to group customers with similar interests, or to group genes with related functionalities. Currently, a challenge for clustering-techniques are especially high dimensional feature-spaces. Due to modern facilities of data collection, real data sets usually contain many features. These features are often noisy or exhibit correlations among each other. However, since these effects in different parts of the data set are differently relevant, irrelevant features cannot be discarded in advance. The selection of relevant features must therefore be integrated into the data mining technique. Since about 10 years, specialized clustering approaches have been developed to cope with problems in high dimensional data better than classic clustering approaches. Often, however, the different problems of very different nature are not distinguished from one another. A main objective of this thesis is therefore a systematic classification of the diverse approaches developed in recent years according to their task definition, their basic strategy, and their algorithmic approach. We discern as main categories the search for clusters (i) w.r.t. closeness of objects in axis-parallel subspaces, (ii) w.r.t. common behavior (patterns) of objects in axis-parallel subspaces, and (iii) w.r.t. closeness of objects in arbitrarily oriented subspaces (so called correlation cluster). For the third category, the remaining parts of the thesis describe novel approaches. A first approach is the adaptation of density-based clustering to the problem of correlation clustering. The starting point here is the first density-based approach in this field, the algorithm 4C. Subsequently, enhancements and variations of this approach are discussed allowing for a more robust, more efficient, or more effective behavior or even find hierarchies of correlation clusters and the corresponding subspaces. The density-based approach to correlation clustering, however, is fundamentally unable to solve some issues since an analysis of local neighborhoods is required. This is a problem in high dimensional data. Therefore, a novel method is proposed tackling the correlation clustering problem in a global approach. Finally, a method is proposed to derive models for correlation clusters to allow for an interpretation of the clusters and facilitate more thorough analysis in the corresponding domain science. Finally, possible applications of these models are proposed and discussed.Knowledge Discovery in Databases (KDD) ist der Prozess der automatischen Extraktion von Wissen aus großen Datenmengen, das gültig, bisher unbekannt und potentiell nützlich für eine gegebene Anwendung ist. Der zentrale Schritt des KDD-Prozesses ist das Anwenden von Data Mining-Techniken, um nützliche Beziehungen und Zusammenhänge in einer aufbereiteten Datenmenge aufzudecken. Eine der wichtigsten Techniken des Data Mining ist die Cluster-Analyse (Clustering). Dabei sollen die Objekte einer Datenbank in Gruppen (Cluster) partitioniert werden, so dass Objekte eines Clusters möglichst ähnlich und Objekte verschiedener Cluster möglichst unähnlich zu einander sind. Hier können beispielsweise Gruppen von Kunden identifiziert werden, die ähnliche Interessen haben, oder Gruppen von Genen, die ähnliche Funktionalitäten besitzen. Eine aktuelle Herausforderung für Clustering-Verfahren stellen hochdimensionale Feature-Räume dar. Reale Datensätze beinhalten dank moderner Verfahren zur Datenerhebung häufig sehr viele Merkmale (Features). Teile dieser Merkmale unterliegen oft Rauschen oder Abhängigkeiten und können meist nicht im Vorfeld ausgesiebt werden, da diese Effekte in Teilen der Datenbank jeweils unterschiedlich ausgeprägt sind. Daher muss die Wahl der Features mit dem Data-Mining-Verfahren verknüpft werden. Seit etwa 10 Jahren werden vermehrt spezialisierte Clustering-Verfahren entwickelt, die mit den in hochdimensionalen Feature-Räumen auftretenden Problemen besser umgehen können als klassische Clustering-Verfahren. Hierbei wird aber oftmals nicht zwischen den ihrer Natur nach im Einzelnen sehr unterschiedlichen Problemen unterschieden. Ein Hauptanliegen der Dissertation ist daher eine systematische Einordnung der in den letzten Jahren entwickelten sehr diversen Ansätze nach den Gesichtspunkten ihrer jeweiligen Problemauffassung, ihrer grundlegenden Lösungsstrategie und ihrer algorithmischen Vorgehensweise. Als Hauptkategorien unterscheiden wir hierbei die Suche nach Clustern (1.) hinsichtlich der Nähe von Cluster-Objekten in achsenparallelen Unterräumen, (2.) hinsichtlich gemeinsamer Verhaltensweisen (Mustern) von Cluster-Objekten in achsenparallelen Unterräumen und (3.) hinsichtlich der Nähe von Cluster-Objekten in beliebig orientierten Unterräumen (sogenannte Korrelations-Cluster). Für die dritte Kategorie sollen in den weiteren Teilen der Dissertation innovative Lösungsansätze entwickelt werden. Ein erster Lösungsansatz basiert auf einer Erweiterung des dichte-basierten Clustering auf die Problemstellung des Korrelations-Clustering. Den Ausgangspunkt bildet der erste dichtebasierte Ansatz in diesem Bereich, der Algorithmus 4C. Anschließend werden Erweiterungen und Variationen dieses Ansatzes diskutiert, die robusteres, effizienteres oder effektiveres Verhalten aufweisen oder sogar Hierarchien von Korrelations-Clustern und den entsprechenden Unterräumen finden. Die dichtebasierten Korrelations-Cluster-Verfahren können allerdings einige Probleme grundsätzlich nicht lösen, da sie auf der Analyse lokaler Nachbarschaften beruhen. Dies ist in hochdimensionalen Feature-Räumen problematisch. Daher wird eine weitere Neuentwicklung vorgestellt, die das Korrelations-Cluster-Problem mit einer globalen Methode angeht. Schließlich wird eine Methode vorgestellt, die Cluster-Modelle für Korrelationscluster ableitet, so dass die gefundenen Cluster interpretiert werden können und tiefergehende Untersuchungen in der jeweiligen Fachdisziplin zielgerichtet möglich sind. Mögliche Anwendungen dieser Modelle werden abschließend vorgestellt und untersucht

Subspace clustering for situation assessment in aquatic drones

We propose a novel methodology based on subspace clustering for detecting, modeling and interpreting aquatic drone states in the context of autonomous water monitoring. It enables both more informative and focused analysis of the large amounts of data collected by the drone, and enhanced situation awareness, which can be exploited by operators and drones to improve decision making and autonomy. The approach is completely data-driven and unsupervised. It takes unlabeled sensor traces from several water monitoring missions and returns both a set of sparse drone state models and a clustering of data samples according to these models. We tested the methodology on a real dataset containing data of six different missions, two rivers and four lakes in different countries, for about 5.5 hours of navigation. Results show that the methodology is able to recognize known states “in/out of the water”, “up- stream/downstream navigation” and “manual/autonomous drive”, and to discover meaningful unknown states from their data-based properties, enabling novelty detection

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

An approach to clustering biological phenotypes /

Recently emerging approaches to high-throughput phenotyping have become important tools in unraveling the biological basis of agronomically and medically important phenotypes. These experiments produce very large sets of either low or high-dimensional data. Finding clusters in the entire space of high-dimensional data (HDD) is a challenging task, because the relative distances between any two objects converge to zero with increasing dimensionality. Additionally, real data may not be mathematically well behaved. Finally, many clusters are expected on biological grounds to be "natural" -- that is, to have irregular, overlapping boundaries in different subsets of the dimensions. More precisely, the natural clusters of the data could differ in shape, size, density, and dimensionality; and they might not be disjoint. In principle, clustering such data could be done by dimension reduction methods. However, these methods convert many dimensions to a smaller set of dimensions that make the clustering results difficult to interpret and may also lead to a significant loss of information. Another possible approach is to find subspaces (subsets of dimensions) in the entire data space of the HDD. However, the existing subspace methods don't discover natural clusters. Therefore, in this dissertation I propose a novel data preprocessing method, demonstrating that a group of phenotypes are interdependent, and propose a novel density-based subspace clustering algorithm for high-dimensional data, called Dynamic Locally Density Adaptive Scalable Subspace Clustering (DynaDASC). This algorithm is relatively locally density adaptive, scalable, dynamic, and nonmetric in nature, and discovers natural clusters.Dr. Toni Kazic, Dissertation Supervisor.|Includes vita.Includes bibliographical references (pages 62-73)