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Some approaches to best-match file searching

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TLDR
Three file structures are presented together with their corresponding search algorithms, which are intended to reduce the number of comparisons required to achieve the desired result.
Abstract
The problem of searching the set of keys in a file to find a key which is closest to a given query key is discussed. After “closest,” in terms of a metric on the the key space, is suitably defined, three file structures are presented together with their corresponding search algorithms, which are intended to reduce the number of comparisons required to achieve the desired result. These methods are derived using certain inequalities satisfied by metrics and by graph-theoretic concepts. Some empirical results are presented which compare the efficiency of the methods.

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Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics

TL;DR: This wide-ranging, thoroughly detailed volume is self-contained and intended for researchers and graduate students, and will prove an invaluable reference tool.
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The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient

TL;DR: This paper discusses the new Omni-technique, which allows to build a variety of dynamic Metric Access Methods based on a number of selected objects from the dataset, used as global reference objects, known as the Omni-family of metric access methods.
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The choice of reference points in best-match file searching

TL;DR: The proper choice of reference points is discussed and the previous algorithm to use more than one reference point is extended and it is shown that reference points should be located outside of data clusters.
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Indexing methods for approximate dictionary searching: Comparative analysis

TL;DR: A taxonomy that classifies all methods into direct methods and sequence-based filtering methods for approximate dictionary searching is introduced, which focuses on infrequently updated dictionaries, which are used primarily for retrieval.
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Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions with an Application to Multicomponent Texture Discrimination

TL;DR: The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure.
References
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Book

Perceptrons: An Introduction to Computational Geometry

TL;DR: The aim of this book is to seek general results from the close study of abstract version of devices known as perceptrons.
Book

Shift register sequences

TL;DR: The Revised Edition of Shift Register Sequences contains a comprehensive bibliography of some 400 entries which cover the literature concerning the theory and applications of shift register sequences.
Journal ArticleDOI

An Analysis of Some Graph Theoretical Cluster Techniques

TL;DR: Several graph theoretic cluster techniques aimed at the automatic generation of thesauri for information retrieval systems are explored and two algorithms have been tested that find maximal complete subgraphs.
Journal ArticleDOI

Scatter storage techniques

TL;DR: L'article donne une presentation didactique sur les methodes connues utilisees par ceux qui ecrivent les assembleurs and compilateurs de maniere a reduire les temps de recherche dans les tables de symboles.
Journal ArticleDOI

Corrections to Bierstone's Algorithm for Generating Cliques

TL;DR: The counterexamples to their and the modified version of the Bierstone algorithm for finding the set of cliques of a finite undirected linear graph are presented.
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