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Author: Leonard Kaufman
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 376
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Book Description
Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix.
Author: Leonard Kaufman
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 376
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Book Description
Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix.
Author: Leonard Kaufman
Publisher: John Wiley & Sons
ISBN: 0470317485
Category : Mathematics
Languages : en
Pages : 368
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Book Description
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
Author: Jure Leskovec
Publisher: Cambridge University Press
ISBN: 1107077230
Category : Computers
Languages : en
Pages : 480
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Book Description
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Author: Jeremy Kubica
Publisher:
ISBN:
Category : Cluster analysis
Languages : en
Pages : 16
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Book Description
Abstract: "Discovering underlying structure from co-occurrence data is an important task in many fields, including: insurance, intelligence, criminal investigation, epidemiology, human resources, and marketing. For example a store may wish to identify underlying sets of items purchased together or a human resources department may wish to identify groups of employees that collaborate with each other. Previously Kubica et. al. presented the group detection algorithm (GDA) -- an algorithm for finding underlying groupings of entities from co-occurrence data. This algorithm is based on a probabilistic generative model and produces coherent groups that are consistent with prior knowledge. Unfortunately, the optimization used in GDA is slow, making it potentially infeasible [sic] for many real world data sets. For example, in the co-publication domain the MEDLINE database of medical publications alone contains over 2 million papers published within just a 5 year period, 1995-1999 [14]. To this end, we present k-groups -- an algorithm that uses an approach similar to that of k-means (hard clustering and localized updates) to significantly accelerate the discovery of the underlying groups while retaining GDA's probabilistic model. In addition, we show that k-groups is guaranteed to converge to a local minimum. We also compare the performance of GDA and k-groups on several real world and artificial data sets, showing the k-groups' sacrifice in solution quality is significantly offset by its increase in speed. This trade-off makes group detection tractable on significantly larger data sets."
Author: Leonard Kaufman
Publisher:
ISBN:
Category :
Languages : en
Pages : 342
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Book Description
Author: Jacob Kogan
Publisher: Cambridge University Press
ISBN: 1139460048
Category : Computers
Languages : en
Pages : 15
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Book Description
There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
Author: Agnivesh
Publisher: A.K. Publications
ISBN: 9789421015015
Category :
Languages : en
Pages : 0
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Book Description
Contemporary researchers find a scenario where almost entire humanity, barring a small percentage of it, is creating data, storing data and using data in a very large scale unstopped. The human race has become data dependent as never before. If infinite had certain defined limits big data would have been a synonym to infinite or almost tending to it in due course of time. Researchers are tackling with the analytics of this big data for making it most useful by evolving various methods. It is growingly desired to reduce infinitesimally the time being taken in the process of analytics. Cloud computing is a computing infrastructure model which implements complex processing in massive scale. It eliminates requirement of maintaining costlier computing hardware and large space requirement. Basic aim behind cloud computing model is to offer processing power, space for data and applications in form of service. Clustering is a powerful big data analytics and prediction technique . The process divides a dataset into groups. These groups are called clusters. Elements of each partition are as close as possible to one another, and elements of different groups are as far as possible from one another . It uncovers hidden information from a dataset. The information is vital for an organization to take right decisions. For example, clustering helps to find out different groups of customers by analyzing their purchasing patterns and choices in trade and business. Similarly, clustering helps in categorizing different species of plants and animals considering their various properties . There are many clustering methods to solve different types of problems. K-means is used widely for clustering . It finds homogenous objects on the basis of distance vectors suited to small datasets. Pre-specifying clusters count and a dataset are the two inputs to the process. By applying trial-and-error method, it finds number of clusters accurately for a given dataset. Moreover, initial centres are selected randomly. This is initialization step of the algorithm. Second step is classification which measures Euclidean distance between these centres and objects. An objects is allocated to its closest centre. Then, average of the points of each cluster is calculated. These averages or means are new centres of the clusters. Final step is convergence step. The process stops as soon as no points migrate from one cluster to another.
Author: Haiyun Bian
Publisher:
ISBN:
Category :
Languages : en
Pages : 152
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Book Description
Data mining focuses on finding previously unknown yet potentially useful, hidden patterns from large amounts of data. Clustering is one of the most commonly used unsupervised data mining techniques, and it has been successfully applied to find groups of similar data points in many applications. However, conventional clustering algorithms sometimes fail to find meaningful clusters when the dataset has dozens of attributes because the high dimensionality makes the data space very noisy. Subspace clustering is a solution to this problem that can find clusters in subsets of all the dimensions. Different subspace clusters may be formed in different subsets of dimensions, and a single data point may belong to multiple subspace clusters. A subspace clustering algorithm not only searches for the clusters, but also finds the subspaces where each individual cluster exists. Allowing overlapping of clusters in the object space and in the attribute space increases the complexity of the search algorithms exponentially and also makes the interpretation of relationships among clusters very difficult. In this thesis, we propose new subspace clustering algorithms that can find overlapping subspace clusters satisfying certain quantitative and qualitative properties. These properties an be defined by the domain users so that the search focuses only on those clusters that have some significance for the users. Molding of the search to find only clusters with specific properties has the advantage that the property itself, or its derivatives, can be used to prune away the uninteresting hypotheses at an early stage of the search. Various pruning strategies are presented in the thesis for different clusters properties to make the search more efficient. In many situations, the total number of subspace clusters having the desired properties is very large, which not only adds burden to the search, but also makes the analysis on the results very difficult. In this thesis, we present ways to impose a lattice structure on all the found clusters, and we show that the lattice facilitates the discovery of other knowledge embedded in the data. We also propose another solution to this problem by creating a condensed representation of all the clusters, that is, we find only a subset of all the clusters from which all other clusters having the desired properties can be inferred. For validation of our algorithms, we tested our algorithms on both the synthetic and the real pplication data. The results suggest that the algorithms are very useful in many application domains, such as with gene expression data and some standard datasets from the machine learning repository. The emerging infrastructure of distributed databases requires algorithms to be designed for mining meaningful patterns in data located at different sites. Due to security and privacy concerns, it is not always feasible to send all datasets to a centralized site to accomplish the mining task. An alternate solution is to have each site perform some computation locally, and exchange minimum amount of information with the other sites. We focus on finding subspace clusters in horizontally partitioned databases. The global computation is decomposed into localized computations on each participating site. We present the detailed decomposition algorithm as well as the format of message exchanges between the sites. Both theoretical and empirical validation of our proposed scheme is provided, showing that our algorithm can find all target patterns from the distributed datasets. Overall, the research presented in this thesis provides many insights into theoretically and empirically characterizing the problem of subspace clustering. The subspace clustering algorithms proposed in this thesis are expected to be useful in solving data mining problems in many applications.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 191
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Book Description
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Author: Dzejla Medjedovic
Publisher: Simon and Schuster
ISBN: 1638356564
Category : Computers
Languages : en
Pages : 302
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Book Description
Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
