Academic literature on the topic 'Density clustering'

When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as Spectral Clustering, and theoretically founded to optimize a proposed density criterion of clusterings. Through experiments on synthetic and real-world data, we demonstrate that our approach provides robust and reliable clusterings.

<p>At present, insider threat detection requires a series of complex projects, and has certain limitations in practical applications; in order to reduce the complexity of the model, most studies ignore the timing of user behavior and fail to identify internal attacks that last for a period of time. In addition, companies usually categorize the behavior data generated by all users and store them in different databases. How to collaboratively process large-scale heterogeneous log files and extract characteristic data that accurately reflects user behavior is a difficult point in current research. In order to optimize the parameter selection of the DBSCAN algorithm, this paper proposes a Psychometric Data & Attack Threat Density Based Spatial Clustering of Applications with Noise algorithm (PD&AT-DBSCAN). This algorithm can improve the accuracy of clustering results. The simulation results show that this algorithm is better than the traditional DBSCAN algorithm in terms of Rand index and normalized mutual information.</p> <p>&nbsp;</p>

<p>At present, insider threat detection requires a series of complex projects, and has certain limitations in practical applications; in order to reduce the complexity of the model, most studies ignore the timing of user behavior and fail to identify internal attacks that last for a period of time. In addition, companies usually categorize the behavior data generated by all users and store them in different databases. How to collaboratively process large-scale heterogeneous log files and extract characteristic data that accurately reflects user behavior is a difficult point in current research. In order to optimize the parameter selection of the DBSCAN algorithm, this paper proposes a Psychometric Data & Attack Threat Density Based Spatial Clustering of Applications with Noise algorithm (PD&AT-DBSCAN). This algorithm can improve the accuracy of clustering results. The simulation results show that this algorithm is better than the traditional DBSCAN algorithm in terms of Rand index and normalized mutual information.</p> <p>&nbsp;</p>

Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to determine the density, and propose a new clustering algorithm, called Correlative Density-based Clustering (CDC). The new algorithm computes the density with a modified way and determines the parameters based on the inherent structure of data points. Experiments on artificial datasets and real datasets demonstrate the simplicity and effectiveness of the proposed approach.

Discovering densely-populated regions in a dataset of data points is an essential task for density-based clustering. To do so, it is often necessary to calculate each data point’s local density in the dataset. Various definitions for the local density have been proposed in the literature. These definitions can be divided into two categories: Radius-based and k Nearest Neighbors-based. In this study, we find the commonality between these two types of definitions and propose a canonical form for the local density. With the canonical form, the pros and cons of the existing definitions can be better explored, and new definitions for the local density can be derived and investigated.

Data clustering is a data analysis technique that groups data based on a measure of similarity. When data is well clustered the similarities between the objects in the same group are high, while the similarities between objects in different groups are low. The data clustering technique is widely applied in a variety of areas such as bioinformatics, image segmentation and market research. This project conducted an in-depth study on data clustering with focus on density-based clustering methods. The latest density-based (CFSFDP) algorithm is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities. This method has been examined, experimented, and improved. These methods (KNN-based, Gaussian Kernel-based and Iterative Gaussian Kernel-based) are applied in this project to improve (CFSFDP) density-based clustering. The methods are applied to four milestone datasets and the results are analyzed and compared.

Improvements in technology, enables us to store large amounts of data in warehouses. In parallel, the need for processing this vast amount of raw data and translating it into interpretable information also increases. A commonly used solution method for the described problem in data mining is clustering. We propose "
Density Based Clustering Using Mathematical Morphology"
(DBCM) algorithm as an effective clustering method for extracting arbitrary shaped clusters of noisy numerical data in a reasonable time. This algorithm is predicated on the analogy between images and data warehouses. It applies grayscale morphology which is an image processing technique on multidimensional data. In this study we evaluated the performance of the proposed algorithm on both synthetic and real data and observed that the algorithm produces successful and interpretable results with appropriate parameters. In addition, we computed the computational complexity to be linear on number of data points for low dimensional data and exponential on number of dimensions for high dimensional data mainly due to the morphology operations.

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.

Data intensive applications like biology, medicine, and neuroscience require effective and efficient data mining technologies. Advanced data acquisition methods produce a constantly increasing volume and complexity. As a consequence, the need of new data mining technologies to deal with complex data has emerged during the last decades. In this thesis, we focus on the data mining task of clustering in which objects are separated in different groups (clusters) such that objects inside a cluster are more similar than objects in different clusters. Particularly, we consider density-based clustering algorithms and their applications in biomedicine. The core idea of the density-based clustering algorithm DBSCAN is that each object within a cluster must have a certain number of other objects inside its neighborhood. Compared with other clustering algorithms, DBSCAN has many attractive benefits, e.g., it can detect clusters with arbitrary shape and is robust to outliers, etc. Thus, DBSCAN has attracted a lot of research interest during the last decades with many extensions and applications. In the first part of this thesis, we aim at developing new algorithms based on the DBSCAN paradigm to deal with the new challenges of complex data, particularly expensive distance measures and incomplete availability of the distance matrix. Like many other clustering algorithms, DBSCAN suffers from poor performance when facing expensive distance measures for complex data. To tackle this problem, we propose a new algorithm based on the DBSCAN paradigm, called Anytime Density-based Clustering (A-DBSCAN), that works in an anytime scheme: in contrast to the original batch scheme of DBSCAN, the algorithm A-DBSCAN first produces a quick approximation of the clustering result and then continuously refines the result during the further run. Experts can interrupt the algorithm, examine the results, and choose between (1) stopping the algorithm at any time whenever they are satisfied with the result to save runtime and (2) continuing the algorithm to achieve better results. Such kind of anytime scheme has been proven in the literature as a very useful technique when dealing with time consuming problems. We also introduced an extended version of A-DBSCAN called A-DBSCAN-XS which is more efficient and effective than A-DBSCAN when dealing with expensive distance measures. Since DBSCAN relies on the cardinality of the neighborhood of objects, it requires the full distance matrix to perform. For complex data, these distances are usually expensive, time consuming or even impossible to acquire due to high cost, high time complexity, noisy and missing data, etc. Motivated by these potential difficulties of acquiring the distances among objects, we propose another approach for DBSCAN, called Active Density-based Clustering (Act-DBSCAN). Given a budget limitation B, Act-DBSCAN is only allowed to use up to B pairwise distances ideally to produce the same result as if it has the entire distance matrix at hand. The general idea of Act-DBSCAN is that it actively selects the most promising pairs of objects to calculate the distances between them and tries to approximate as much as possible the desired clustering result with each distance calculation. This scheme provides an efficient way to reduce the total cost needed to perform the clustering. Thus it limits the potential weakness of DBSCAN when dealing with the distance sparseness problem of complex data. As a fundamental data clustering algorithm, density-based clustering has many applications in diverse fields. In the second part of this thesis, we focus on an application of density-based clustering in neuroscience: the segmentation of the white matter fiber tracts in human brain acquired from Diffusion Tensor Imaging (DTI). We propose a model to evaluate the similarity between two fibers as a combination of structural similarity and connectivity-related similarity of fiber tracts. Various distance measure techniques from fields like time-sequence mining are adapted to calculate the structural similarity of fibers. Density-based clustering is used as the segmentation algorithm. We show how A-DBSCAN and A-DBSCAN-XS are used as novel solutions for the segmentation of massive fiber datasets and provide unique features to assist experts during the fiber segmentation process.
Datenintensive Anwendungen wie Biologie, Medizin und Neurowissenschaften erfordern effektive und effiziente Data-Mining-Technologien. Erweiterte Methoden der Datenerfassung erzeugen stetig wachsende Datenmengen und Komplexit\"at. In den letzten Jahrzehnten hat sich daher ein Bedarf an neuen Data-Mining-Technologien f\"ur komplexe Daten ergeben. In dieser Arbeit konzentrieren wir uns auf die Data-Mining-Aufgabe des Clusterings, in der Objekte in verschiedenen Gruppen (Cluster) getrennt werden, so dass Objekte in einem Cluster untereinander viel \"ahnlicher sind als Objekte in verschiedenen Clustern. Insbesondere betrachten wir dichtebasierte Clustering-Algorithmen und ihre Anwendungen in der Biomedizin. Der Kerngedanke des dichtebasierten Clustering-Algorithmus DBSCAN ist, dass jedes Objekt in einem Cluster eine bestimmte Anzahl von anderen Objekten in seiner Nachbarschaft haben muss. Im Vergleich mit anderen Clustering-Algorithmen hat DBSCAN viele attraktive Vorteile, zum Beispiel kann es Cluster mit beliebiger Form erkennen und ist robust gegen\"uber Ausrei{\ss}ern. So hat DBSCAN in den letzten Jahrzehnten gro{\ss}es Forschungsinteresse mit vielen Erweiterungen und Anwendungen auf sich gezogen. Im ersten Teil dieser Arbeit wollen wir auf die Entwicklung neuer Algorithmen eingehen, die auf dem DBSCAN Paradigma basieren, um mit den neuen Herausforderungen der komplexen Daten, insbesondere teurer Abstandsma{\ss}e und unvollst\"andiger Verf\"ugbarkeit der Distanzmatrix umzugehen. Wie viele andere Clustering-Algorithmen leidet DBSCAN an schlechter Per- formanz, wenn es teuren Abstandsma{\ss}en f\"ur komplexe Daten gegen\"uber steht. Um dieses Problem zu l\"osen, schlagen wir einen neuen Algorithmus vor, der auf dem DBSCAN Paradigma basiert, genannt Anytime Density-based Clustering (A-DBSCAN), der mit einem Anytime Schema funktioniert. Im Gegensatz zu dem urspr\"unglichen Schema DBSCAN, erzeugt der Algorithmus A-DBSCAN zuerst eine schnelle Ann\"aherung des Clusterings-Ergebnisses und verfeinert dann kontinuierlich das Ergebnis im weiteren Verlauf. Experten k\"onnen den Algorithmus unterbrechen, die Ergebnisse pr\"ufen und w\"ahlen zwischen (1) Anhalten des Algorithmus zu jeder Zeit, wann immer sie mit dem Ergebnis zufrieden sind, um Laufzeit sparen und (2) Fortsetzen des Algorithmus, um bessere Ergebnisse zu erzielen. Eine solche Art eines "Anytime Schemas" ist in der Literatur als eine sehr n\"utzliche Technik erprobt, wenn zeitaufwendige Problemen anfallen. Wir stellen auch eine erweiterte Version von A-DBSCAN als A-DBSCAN-XS vor, die effizienter und effektiver als A-DBSCAN beim Umgang mit teuren Abstandsma{\ss}en ist. Da DBSCAN auf der Kardinalit\"at der Nachbarschaftsobjekte beruht, ist es notwendig, die volle Distanzmatrix auszurechen. F\"ur komplexe Daten sind diese Distanzen in der Regel teuer, zeitaufwendig oder sogar unm\"oglich zu errechnen, aufgrund der hohen Kosten, einer hohen Zeitkomplexit\"at oder verrauschten und fehlende Daten. Motiviert durch diese m\"oglichen Schwierigkeiten der Berechnung von Entfernungen zwischen Objekten, schlagen wir einen anderen Ansatz f\"ur DBSCAN vor, namentlich Active Density-based Clustering (Act-DBSCAN). Bei einer Budgetbegrenzung B, darf Act-DBSCAN nur bis zu B ideale paarweise Distanzen verwenden, um das gleiche Ergebnis zu produzieren, wie wenn es die gesamte Distanzmatrix zur Hand h\"atte. Die allgemeine Idee von Act-DBSCAN ist, dass es aktiv die erfolgversprechendsten Paare von Objekten w\"ahlt, um die Abst\"ande zwischen ihnen zu berechnen, und versucht, sich so viel wie m\"oglich dem gew\"unschten Clustering mit jeder Abstandsberechnung zu n\"ahern. Dieses Schema bietet eine effiziente M\"oglichkeit, die Gesamtkosten der Durchf\"uhrung des Clusterings zu reduzieren. So schr\"ankt sie die potenzielle Schw\"ache des DBSCAN beim Umgang mit dem Distance Sparseness Problem von komplexen Daten ein. Als fundamentaler Clustering-Algorithmus, hat dichte-basiertes Clustering viele Anwendungen in den unterschiedlichen Bereichen. Im zweiten Teil dieser Arbeit konzentrieren wir uns auf eine Anwendung des dichte-basierten Clusterings in den Neurowissenschaften: Die Segmentierung der wei{\ss}en Substanz bei Faserbahnen im menschlichen Gehirn, die vom Diffusion Tensor Imaging (DTI) erfasst werden. Wir schlagen ein Modell vor, um die \"Ahnlichkeit zwischen zwei Fasern als einer Kombination von struktureller und konnektivit\"atsbezogener \"Ahnlichkeit von Faserbahnen zu beurteilen. Verschiedene Abstandsma{\ss}e aus Bereichen wie dem Time-Sequence Mining werden angepasst, um die strukturelle \"Ahnlichkeit von Fasern zu berechnen. Dichte-basiertes Clustering wird als Segmentierungsalgorithmus verwendet. Wir zeigen, wie A-DBSCAN und A-DBSCAN-XS als neuartige L\"osungen f\"ur die Segmentierung von sehr gro{\ss}en Faserdatens\"atzen verwendet werden, und bieten innovative Funktionen, um Experten w\"ahrend des Fasersegmentierungsprozesses zu unterst\"utzen.

Global Navigation Satellite Systems (GNSS) is in widespread use around the world for numerous purposes. Even though it was first developed for military purposes, nowadays, the civilian use has surpassed it by far. It has evolved to its finest state in recent days and still being developed further towards pinpoint accuracy. With all the improvements, several vulnerabilities have been discovered by researchers and exploited by the attackers. Several countermeasures have been and still being implemented to secure the GNSS. Studies show that GNSS-based receivers are still vulnerable to a very fundamentally simple, yet effective, attack; known as the replay attack. The replay attack is particularly harmful since the attacker could make the receiver calculate an inaccurate position, without even breaking the encryption or without employing any sophisticated technique. The Multiple Combinations of Satellites and Systems (MCSS) test is a powerful test against replay attacks on GNSS. However, detecting and identifying multiple attacking signals and determining the correct position of the receiver simultaneously remain as a challenge. In this study, after the implementation of MCSS test, a mechanism to detect the attacker controlled signals has been demonstrated. Furthermore, applying a clustering algorithm on the product of MCSS test, a method of correctly determining the position, nullifying the adversarial effects has also been presented in this report.

This chapter explores how business groups can be viewed as networks; whether and how some groups are more “network-like” than others; and how formal network concepts and analytic methods may facilitate the study of a number of salient problems in business-group research. Much of the business-group literature treats a firm’s affiliation with a group as an “all or nothing” dichotomy. The network lens, however, forces the analyst to unpack the coarse dichotomy of “group” and “stand-alone” into an array of constituent relations, equivalences, and complementarities, which can in turn be mapped to outcomes such as strategy, operations, and performance. We first consider how attention to such formal network properties as density, connectivity, centrality, and clustering may advance business-group research. We then examine the degree to which a number of group configurations approximate the ideal type “network form”—a leading-edge mode of economic organization in the global economy.