630 research outputs found
Multivariate discretization of continuous valued attributes.
The area of Knowledge discovery and data mining is growing rapidly. Feature Discretization is a crucial issue in Knowledge Discovery in Databases (KDD), or Data Mining because most data sets used in real world applications have features with continuously values. Discretization is performed as a preprocessing step of the data mining to make data mining techniques useful for these data sets. This thesis addresses discretization issue by proposing a multivariate discretization (MVD) algorithm. It begins withal number of common discretization algorithms like Equal width discretization, Equal frequency discretization, NaĂŻve; Entropy based discretization, Chi square discretization, and orthogonal hyper planes. After that comparing the results achieved by the multivariate discretization (MVD) algorithm with the accuracy results of other algorithms. This thesis is divided into six chapters, covering a few common discretization algorithms and tests these algorithms on a real world datasets which varying in size and complexity, and shows how data visualization techniques will be effective in determining the degree of complexity of the given data set. We have examined the multivariate discretization (MVD) algorithm with the same data sets. After that we have classified discrete data using artificial neural network single layer perceptron and multilayer perceptron with back propagation algorithm. We have trained the Classifier using the training data set, and tested its accuracy using the testing data set. Our experiments lead to better accuracy results with some data sets and low accuracy results with other data sets, and this is subject ot the degree of data complexity then we have compared the accuracy results of multivariate discretization (MVD) algorithm with the results achieved by other discretization algorithms. We have found that multivariate discretization (MVD) algorithm produces good accuracy results in comparing with the other discretization algorithm
Data Mining Using the Crossing Minimization Paradigm
Our ability and capacity to generate, record and store multi-dimensional, apparently
unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining.
Because of the size, and complexity of the problem, practical data mining problems are
best attempted using automatic means.
Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes.
In this dissertation, a novel fast and white noise tolerant data mining solution is
proposed based on the Crossing Minimization (CM) paradigm; the solution works for
one-way as well as two-way clustering for discovering overlapping biclusters. For
decades the CM paradigm has traditionally been used for graph drawing and VLSI
(Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains.
Two other interesting and hard problems also addressed in this dissertation are (i) the
Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth
Minimization (BWM) problem of sparse matrices. The proposed CM technique is
demonstrated to provide very convincing results while attempting to solve the said
problems using real public domain data.
Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has
been observed during 1989-97 between cotton yield and pesticide consumption in
Pakistan showing unexpected periods of negative correlation. By applying the
indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis
Random Projection in Deep Neural Networks
This work investigates the ways in which deep learning methods can benefit
from random projection (RP), a classic linear dimensionality reduction method.
We focus on two areas where, as we have found, employing RP techniques can
improve deep models: training neural networks on high-dimensional data and
initialization of network parameters. Training deep neural networks (DNNs) on
sparse, high-dimensional data with no exploitable structure implies a network
architecture with an input layer that has a huge number of weights, which often
makes training infeasible. We show that this problem can be solved by
prepending the network with an input layer whose weights are initialized with
an RP matrix. We propose several modifications to the network architecture and
training regime that makes it possible to efficiently train DNNs with learnable
RP layer on data with as many as tens of millions of input features and
training examples. In comparison to the state-of-the-art methods, neural
networks with RP layer achieve competitive performance or improve the results
on several extremely high-dimensional real-world datasets. The second area
where the application of RP techniques can be beneficial for training deep
models is weight initialization. Setting the initial weights in DNNs to
elements of various RP matrices enabled us to train residual deep networks to
higher levels of performance
On the Nature and Types of Anomalies: A Review
Anomalies are occurrences in a dataset that are in some way unusual and do
not fit the general patterns. The concept of the anomaly is generally
ill-defined and perceived as vague and domain-dependent. Moreover, despite some
250 years of publications on the topic, no comprehensive and concrete overviews
of the different types of anomalies have hitherto been published. By means of
an extensive literature review this study therefore offers the first
theoretically principled and domain-independent typology of data anomalies, and
presents a full overview of anomaly types and subtypes. To concretely define
the concept of the anomaly and its different manifestations, the typology
employs five dimensions: data type, cardinality of relationship, anomaly level,
data structure and data distribution. These fundamental and data-centric
dimensions naturally yield 3 broad groups, 9 basic types and 61 subtypes of
anomalies. The typology facilitates the evaluation of the functional
capabilities of anomaly detection algorithms, contributes to explainable data
science, and provides insights into relevant topics such as local versus global
anomalies.Comment: 38 pages (30 pages content), 10 figures, 3 tables. Preprint; review
comments will be appreciated. Improvements in version 2: Explicit mention of
fifth anomaly dimension; Added section on explainable anomaly detection;
Added section on variations on the anomaly concept; Various minor additions
and improvement
Graph based Anomaly Detection and Description: A Survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the âwhyâ, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field
Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents
Few technological ideas have captivated the minds of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and biological phenomena. In this dissertation, we capitalize on these achievements and use machine learning to study drug receptor sites and design drugs to target these sites. First, we analyze the significance of various single nucleotide variations and assess their rate of contribution to cancer. Following that, we used a portfolio of machine learning and data science approaches to design new drugs to target protein kinase inhibitors. We show that these techniques exhibit strong promise in aiding cancer research and drug discovery
Comprehensible and Robust Knowledge Discovery from Small Datasets
Die Wissensentdeckung in Datenbanken (âKnowledge Discovery in Databasesâ, KDD) zielt darauf ab, nĂŒtzliches Wissen aus Daten zu extrahieren. Daten können eine Reihe
von Messungen aus einem realen Prozess reprÀsentieren oder eine Reihe von Eingabe-
Ausgabe-Werten eines Simulationsmodells. Zwei hĂ€ufig widersprĂŒchliche Anforderungen
an das erworbene Wissen sind, dass es (1) die Daten möglichst exakt zusammenfasst und
(2) in einer gut verstĂ€ndlichen Form vorliegt. EntscheidungsbĂ€ume (âDecision Treesâ) und
Methoden zur Entdeckung von Untergruppen (âSubgroup Discoveryâ) liefern Wissenszusammenfassungen in Form von Hyperrechtecken; diese gelten als gut verstĂ€ndlich.
Um die Bedeutung einer verstÀndlichen Datenzusammenfassung zu demonstrieren,
erforschen wir Dezentrale intelligente Netzsteuerung â ein neues System, das die Bedarfsreaktion in Stromnetzen ohne wesentliche Ănderungen in der Infrastruktur implementiert.
Die bisher durchgefĂŒhrte konventionelle Analyse dieses Systems beschrĂ€nkte sich auf
die BerĂŒcksichtigung identischer Teilnehmer und spiegelte daher die RealitĂ€t nicht ausreichend gut wider. Wir fĂŒhren viele Simulationen mit unterschiedlichen Eingabewerten durch und wenden EntscheidungsbĂ€ume auf die resultierenden Daten an. Mit den daraus resultierenden verstĂ€ndlichen Datenzusammenfassung konnten wir neue Erkenntnisse zum Verhalten der Dezentrale intelligente Netzsteuerung gewinnen.
EntscheidungsbĂ€ume ermöglichen die Beschreibung des Systemverhaltens fĂŒr alle Eingabekombinationen.
Manchmal ist man aber nicht daran interessiert, den gesamten Eingaberaum
zu partitionieren, sondern Bereiche zu finden, die zu bestimmten Ausgabe fĂŒhren
(sog. Untergruppen). Die vorhandenen Algorithmen zum Erkennen von Untergruppen
erfordern normalerweise groĂe Datenmengen, um eine stabile und genaue Ausgabe zu erzielen.
Der Datenerfassungsprozess ist jedoch hÀufig kostspielig. Unser Hauptbeitrag ist die
Verbesserung der Untergruppenerkennung aus DatensÀtzen mit wenigen Beobachtungen.
Die Entdeckung von Untergruppen in simulierten Daten wird als Szenarioerkennung
bezeichnet. Ein hĂ€ufig verwendeter Algorithmus fĂŒr die Szenarioerkennung ist PRIM
(Patient Rule Induction Method). Wir schlagen REDS (Rule Extraction for Discovering
Scenarios) vor, ein neues Verfahren fĂŒr die Szenarioerkennung. FĂŒr REDS, trainieren wir
zuerst ein statistisches Zwischenmodell und verwenden dieses, um eine groĂe Menge
neuer Daten fĂŒr PRIM zu erstellen. Die grundlegende statistische Intuition beschrieben wir
ebenfalls. Experimente zeigen, dass REDS viel besser funktioniert als PRIM fĂŒr sich alleine:
Es reduziert die Anzahl der erforderlichen SimulationslÀufe um 75% im Durchschnitt.
Mit simulierten Daten hat man perfekte Kenntnisse ĂŒber die Eingangsverteilung â eine
Voraussetzung von REDS. Um REDS auf realen Messdaten anwendbar zu machen, haben
wir es mit Stichproben aus einer geschÀtzten multivariate Verteilung der Daten kombiniert.
Wir haben die resultierende Methode in Kombination mit verschiedenen Methoden zur Generierung von Daten experimentell evaluiert. Wir haben dies fĂŒr PRIM und BestInterval â eine weitere reprĂ€sentative Methode zur Erkennung von Untergruppen â gemacht. In den meisten FĂ€llen hat unsere Methodik die QualitĂ€t der entdeckten Untergruppen erhöht
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