Индексирование данных в пространстве большой размерности

Indexing of the data is an essential part of the search problem. Whilst a well studied set of efficient algorithms of indexing and search exist for a data in the spaces of the dimensionality less then 5, these algorithms are inefficient or not applicable for high dimensional spaces. We review existing studies of the problems related to indexing in a high dimensional space for a nearest neighbour search problem. Possible methods of the designated problems solution that are applicable in the areas of data analysis such as clustering and hidden structure elicitation are regarded. The question on the applicability of the various dimensional reduction methods for an indexing and nearest neighbour search problem.Индексирование данных является неотделимой частью задачи поиска. В то время как для данных в пространствах размерностей не более 5 существует хорошо изученный набор эффективных алгоритмов индексации и поиска, для пространств большой размерностей эти алгоритмы оказываются неэффективны или неприменимы. В этом обзоре мы приводим существующие обоснования проблем связанных с индексированием в пространствах большой размерности для задачи поиска ближайшего соседа. Рассматриваются возможные методы решения обозначенных проблем, применимые в областях анализа данных, таких как кластеризация и извлечение скрытых структур. Ставится вопрос о применимости различных методов размерностной редукции к задачи индексирования и поиска ближайшего соседа

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

Adaptive grid based localized learning for multidimensional data

Rapid advances in data-rich domains of science, technology, and business has amplified the computational challenges of Big Data synthesis necessary to slow the widening gap between the rate at which the data is being collected and analyzed for knowledge. This has led to the renewed need for efficient and accurate algorithms, framework, and algorithmic mechanisms essential for knowledge discovery, especially in the domains of clustering, classification, dimensionality reduction, feature ranking, and feature selection. However, data mining algorithms are frequently challenged by the sparseness due to the high dimensionality of the datasets in such domains which is particularly detrimental to the performance of unsupervised learning algorithms. The motivation for the research presented in this dissertation is to develop novel data mining algorithms to address the challenges of high dimensionality, sparseness and large volumes of datasets by using a unique grid-based localized learning paradigm for data movement clustering and classification schema. The grid-based learning is recognized in data mining as these algorithms are inherently efficient since they reduce the search space by partitioning the feature space into effective partitions. However, these approaches have not been successfully devised for supervised learning algorithms or sparseness reduction algorithm as they require careful estimation of grid sizes, partitions and data movement error calculations. Grid-based localized learning algorithms can scale well with an increase in dimensionality and the size of the datasets. To fulfill the goal of designing and developing learning algorithms that can handle data sparseness, high data dimensionality, and large size of data, in a concurrent manner to avoid the feature selection biases, a set of novel data mining algorithms using grid-based localized learning principles are developed and presented. The first algorithm is a unique computational framework for feature ranking that employs adaptive grid-based data shrinking for feature ranking. This method addresses the limitations of existing feature ranking methods by using a scoring function that discovers and exploits dependencies from all the features in the data. Data shrinking principles are established and metricized to capture and exploit dependencies between features. The second core algorithmic contribution is a novel supervised learning algorithm that utilizes grid-based localized learning to build a nonparametric classification model. In this classification model, feature space is divided using uniform/non-uniform partitions and data space subdivision is performed using a grid structure which is then used to build a classification model using grid-based nearest-neighbor learning. The third algorithm is an unsupervised clustering algorithm that is augmented with data shrinking to enhance the clustering performance of the algorithm. This algorithm addresses the limitations of the existing grid-based data shrinking and clustering algorithms by using an adaptive grid-based learning. Multiple experiments on a diversified set of datasets evaluate and discuss the effectiveness of dimensionality reduction, feature selection, unsupervised and supervised learning, and the scalability of the proposed methods compared to the established methods in the literature

Doctor of Philosophy

dissertationWith the tremendous growth of data produced in the recent years, it is impossible to identify patterns or test hypotheses without reducing data size. Data mining is an area of science that extracts useful information from the data by discovering patterns and structures present in the data. In this dissertation, we will largely focus on clustering which is often the first step in any exploratory data mining task, where items that are similar to each other are grouped together, making downstream data analysis robust. Different clustering techniques have different strengths, and the resulting groupings provide different perspectives on the data. Due to the unsupervised nature i.e., the lack of domain experts who can label the data, validation of results is very difficult. While there are measures that compute "goodness" scores for clustering solutions as a whole, there are few methods that validate the assignment of individual data items to their clusters. To address these challenges we focus on developing a framework that can generate, compare, combine, and evaluate different solutions to make more robust and significant statements about the data. In the first part of this dissertation, we present fast and efficient techniques to generate and combine different clustering solutions. We build on some recent ideas on efficient representations of clusters of partitions to develop a well founded metric that is spatially aware to compare clusterings. With the ability to compare clusterings, we describe a heuristic to combine different solutions to produce a single high quality clustering. We also introduce a Markov chain Monte Carlo approach to sample different clusterings from the entire landscape to provide the users with a variety of choices. In the second part of this dissertation, we build certificates for individual data items and study their influence on effective data reduction. We present a geometric approach by defining regions of influence for data items and clusters and use this to develop adaptive sampling techniques to speedup machine learning algorithms. This dissertation is therefore a systematic approach to study the landscape of clusterings in an attempt to provide a better understanding of the data

Coping With New Challengens for Density-Based 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 tasks 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. Beside many others, the density-based clustering notion underlying the algorithm DBSCAN and its hierarchical extension OPTICS has been proposed recently, being one of the most successful approaches to clustering. In this thesis, our aim is to advance the state-of-the-art clustering, especially density-based clustering by identifying novel challenges for density-based clustering and proposing innovative and solid solutions for these challenges. We describe the development of the industrial prototype BOSS (Browsing OPTICS plots for Similarity Search) which is a first step towards developing a comprehensive, scalable and distributed computing solution designed to make the efficiency and analytical capabilities of OPTICS available to a broader audience. For the development of BOSS, several key enhancements of OPTICS are required which are addressed in this thesis. We develop incremental algorithms of OPTICS to efficiently reconstruct the hierarchical clustering structure in frequently updated databases, in particular, when a set of objects is inserted in or deleted from the database. We empirically show that these incremental algorithms yield significant speed-up factors over the original OPTICS algorithm. Furthermore, we propose a novel algorithm for automatic extraction of clusters from hierarchical clustering representations that outperforms comparative methods, and introduce two novel approaches for selecting meaningful representatives, using the density-based concepts of OPTICS and producing better results than the related medoid approach. Another major challenge for density-based clustering is to cope with high dimensional data. Many today's real-world data sets contain a large number of measurements (or features) for a single data object. Usually, global feature reduction techniques cannot be applied to these data sets. Thus, the task of feature selection must be combined with and incooperated into the clustering process. In this thesis, we present original extensions and enhancements of the density-based clustering notion to cope with high dimensional data. In particular, we propose an algorithm called SUBCLU (density based SUBspace CLUstering) that extends DBSCAN to the problem of subspace clustering. SUBCLU efficiently computes all clusters that would have been found if DBSCAN is applied to all possible subspaces of the feature space. An experimental evaluation on real-world data sets illustrates that SUBCLU is more effective than existing subspace clustering algorithms because it is able to find clusters of arbitrary size and shape, and produces determine results. A semi-hierarchical extension of SUBCLU called RIS (Ranking Interesting Subspaces) is proposed that does not compute the subspace clusters directly, but generates a list of subspaces ranked by their clustering characteristics. A hierarchical clustering algorithm can be applied to these interesting subspaces in order to compute a hierarchical (subspace) clustering. A comparative evaluation of RIS and SUBCLU shows that RIS in combination with OPTICS can achieve an information gain over SUBCLU. In addition, we propose the algorithm 4C (Computing Correlation Connected Clusters) that extends the concepts of DBSCAN to compute density-based correlation clusters. 4C benefits from an innovative, well-defined and effective clustering model, outperforming related approaches in terms of clustering quality on real-world data sets.Knowledge Discovery in Databases (KDD) ist der Prozess der (semi-)automatischen Extraktion von Wissen aus Datenbanken, das gültig, bisher unbekannt und potentiell nützlich für eine gegebene Anwendung ist. Der zentrale Schritt des KDD-Prozesses ist das Data Mining. Eine der wichtigsten Aufgaben des Data Mining ist 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. Das dichtebasierte Clustermodell und die darauf aufbauenden Algorithmen DBSCAN und OPTICS sind unter einer Vielzahl anderer Clustering-Ansätze eine der erfolgreichsten Methoden zum Clustering. Im Rahmen dieser Dissertation wollen wir den aktuellen Stand der Technik im Bereich Clustering und speziell im Bereich dichtebasiertes Clustering voranbringen. Dazu erarbeiten wir neue Herausforderungen für das dichtebasierte Clustermodell und schlagen dazu innovative Lösungen vor. Zunächst steht die Entwicklung des industriellen Prototyps BOSS (Browsing OPTICS plots for Similarity Search) im Mittelpunkt dieser Arbeit. BOSS ist ein erster Beitrag zu einer umfassenden, skalierbaren und verteilten Softwarelösung, die eine Nutzung der Effizienzvorteile und die analytischen Möglichkeiten des dichtebasierten, hierarchischen Clustering-Algorithmus OPTICS für ein breites Publikum ermöglichen. Zur Entwicklung von BOSS werden drei entscheidende Erweiterungen von OPTICS benötigt: Wir entwickeln eine inkrementelle Version von OPTICS um nach einem Update der Datenbank (Einfügen/Löschen einer Menge von Objekten) die hierarchische Clustering Struktur effizient zu reorganisieren. Anhand von Experimenten mit synthetischen und realen Daten zeigen wir, dass die vorgeschlagenen, inkrementellen Algorithmen deutliche Beschleunigungsfaktoren gegenüber dem originalen OPTICS-Algorithmus erzielen. Desweiteren schlagen wir einen neuen Algorithmus zur automatischen Clusterextraktion aus hierarchischen Repräsentationen und zwei innovative Methoden zur automatischen Auswahl geeigneter Clusterrepräsentaten vor. Unsere neuen Techniken erzielen bei Tests auf mehreren realen Datenbanken im Vergleich zu den konkurrierenden Verfahren bessere Ergebnisse. Eine weitere Herausforderung für Clustering-Verfahren stellen hochdimensionale Featureräume dar. Reale Datensätze beinhalten dank moderner Verfahren zur Datenerhebung häufig sehr viele Merkmale. Teile dieser Merkmale unterliegen oft Rauschen oder Abhängigkeiten und können meist nicht im Vorfeld ausgesiebt werden, da diese Effekte jeweils in Teilen der Datenbank unterschiedlich ausgeprägt sind. Daher muss die Wahl der Features mit dem Data-Mining-Verfahren verknüpft werden. Im Rahmen dieser Arbeit stellen wir innovative Erweiterungen des dichtebasierten Clustermodells für hochdimensionale Daten vor. Wir entwickeln SUBCLU (dichtebasiertes SUBspace CLUstering), ein auf DBSCAN basierender Subspace Clustering Algorithmus. SUBCLU erzeugt effizient alle Cluster, die gefunden werden, wenn man DBSCAN auf alle möglichen Teilräume des Datensatzes anwendet. Experimente auf realen Daten zeigen, dass SUBCLU effektiver als vergleichbare Algorithmen ist. RIS (Ranking Interesting Subspaces), eine semi-hierarchische Erweiterung von SUBCLU, wird vorgeschlagen, das nicht mehr direkt die Teilraumcluster berechnet, sondern eine Liste von Teilräumen geordnet anhand ihrer Clustering-Qualität erzeugt. Dadurch können hierarchische Partitionierungen auf ausgewählten Teilräumen erzeugt werden. Experimente belegen, dass RIS in Kombination mit OPTICS ein Informationsgewinn gegenüber SUBCLU erreicht. Außerdem stellen wir den neuartigen Korrelationscluster Algorithmus 4C (Computing Correlation Connected Clusters) vor. 4C basiert auf einem innovativen und wohldefinierten Clustermodell und erzielt in unseren Experimenten mit realen Daten bessere Ergebnisse als vergleichbare Clustering-Ansätze

Accurate and efficient clustering algorithms for very large data sets

The ability to mine and extract useful information from large data sets is a common concern for organizations. Data over the internet is rapidly increasing and the importance of development of new approaches to collect, store and mine large amounts of data is significantly increasing. Clustering is one of the main tasks in data mining. Many clustering algorithms have been proposed but there are still clustering problems that have not been addressed in depth especially the clustering problems in large data sets. Clustering in large data sets is important in many applications and such applications include network intrusion detection systems, fraud detection in banking systems, air traffic control, web logs, sensor networks, social networks and bioinformatics. Data sets in these applications contain from hundreds of thousands to hundreds of millions of data points and they may contain hundreds or thousands of attributes. Recent developments in computer hardware allows to store in random access memory and repeatedly read data sets with hundreds of thousands and even millions of data points. This makes possible the use of existing clustering algorithms in such data sets. However, these algorithms require a prohibitively large CPU time and fail to produce an accurate solution. Therefore, it is important to develop clustering algorithms which are accurate and can provide real time clustering in such data sets. This is especially important in a big data era. The aim of this PhD study is to develop accurate and real time algorithms for clustering in very large data sets containing hundreds of thousands and millions of data points. Such algorithms are developed based on the combination of heuristic algorithms with the incremental approach. These algorithms also involve a special procedure to identify dense areas in a data set and compute a subset most informative representative data points in order to decrease the size of a data set. It is the aim of this PhD study to develop the center-based clustering algorithms. The success of these algorithms strongly depends on the choice of starting cluster centers. Different procedures are proposed to generate such centers. Special procedures are designed to identify the most promising starting cluster centers and to restrict their number. New clustering algorithms are evaluated using large data sets available in public domains. Their results will be compared with those obtained using several existing center-based clustering algorithms.Doctor of Philosoph