Hierarchical trees

Part 2 of ebook "Principles of data mining (Third edition)" provides readers with contents including: Chapter 17 - Association rule mining II; Chapter 18 - Association rule mining III, frequent pattern trees; Chapter 19 - Clustering; Chapter 20 - Text mining; Chapter 21 - Classifying streaming data; Chapter 22 - Classifying streaming data II, time-dependent data;...

267p daonhiennhien 03-07-2024 1 1   Download

The book is organized into four major parts. Part I consists of two chapters, one of which is devoted to the main tenets of wireless ad hoc networks, and wireless personal area networks and wireless sensor networks in particular. Part II, most voluminous by far, models and analyzes the performance of single-cluster networks. Part III deals with performance-related aspects of multi-cluster networks utilizing hierarchical, tree-like topologies. Part IV introduces security issues in the context of both single- and multi-cluster networks.

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Appendix E - Hierarchical model. This chapter presents the following content: Basic concepts, tree-structure diagrams, data-retrieval facility, update facility, virtual records, mapping of hierarchies to files, the IMS database system.

60p haojiubujain04 27-07-2023 7 4   Download

Ebook Machine learning algorithms: Part 2 presents the following content: Chapter 8: decision trees and ensemble learning, chapter 9: clustering fundamentals, chapter 10: hierarchical clustering, chapter 11: introduction to recommendation systems, chapter 12: introduction to natural language processing, chapter 13: topic modeling and sentiment analysis in NLP, chapter 14: a brief introduction to deep learning and tensorflow, chapter 15: creating a machine learning architecture.

184p runthenight09 06-05-2023 8 4   Download

Lecture Data mining: Lesson 1. The main topics covered in this chapter include: data warehouses and OLAP (On Line Analytical Processing); association rules mining; clustering - hierarchical and partition approaches; classification - decision trees and bayesian classifiers; sequential pattern mining;... Please refer to the content of document.

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Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes.

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In genomics, hierarchical clustering (HC) is a popular method for grouping similar samples based on a distance measure. HC algorithms do not actually create clusters, but compute a hierarchical representation of the data set.

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Maximum parsimony reconciliation in the duplication-transfer-loss model is a widely-used method for analyzing the evolutionary histories of pairs of entities such as hosts and parasites, symbiont species, and species and genes.

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In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade.

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A hierarchical ordering of a finite number of objects may be stored in a tree data structure. Operations on a hierarchically stored container include: Accessing the root, given an object in the container: Access the parent of the current object, find the degree of the current object, get a reference to a child, attach a new sub-tree to the current object, detach this tree from its parent.

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Recent theoretical and empirical work has identified redundancy as one of the benefits of the reticulate form in the evolution of leaf vein networks. However, we know little about the costs of redundancy or how those costs depend on vein network geometry or topology.

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After completing this chapter, students will be able to: Basic concepts, tree-structure diagrams, data-retrieval facility, update facility, virtual records, mapping of hierarchies to files, the IMS database system.

32p thuongdanguyetan03 18-04-2020 25 1   Download

The breadfruit tree (Artocarpus altilis), despite its food, therapeutic and agroforestry potential, remained underutilized in Benin due to the lack of information on the species. The objective of this study is to carry out the morphological characterization of the species in order to have a better knowledge of its diversity in Benin, an essential step in its safeguarding and its use. Thus, the discriminatory morphological descriptors in Artocarpus altilis in relation to the agro-ecological diversity of Benin have been identified. Foliar, fruit and seed characteristics were studied.

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This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of underlying data while still maintaining reasonable memory allocation.

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A hierarchical ordering of a finite number of objects may be stored in a tree data structure. Operations on a hierarchically stored container include: Accessing the root, given an object in the container: Access the parent of the current object, find the degree of the current object, get a reference to a child, attach a new sub-tree to the current object, detach this tree from its parent.

21p allbymyself_08 22-02-2016 42 2   Download

This paper proposes a mistake-driven mixture method for learning a tag model. The method iteratively performs two procedures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the constructed model. The final tag model is constructed by mixing all the models according to their performance. 1

8p bunthai_1 06-05-2013 40 2   Download

We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-ofart systems.

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In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Machine Translation (SMT). SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximumentropy framework. ...

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Sentences are not just strings of words (or are they ?), they have some (hierarchical) structure. This much is accepted by all grammar formalisms. But how much structure is needed? The more the sentences are like strings the less the need for structure. A certain amount of structure is necessary simply because a clause may embed another clause, or one clause may attach to another clause or parts of it. Leaving this need of structure aside, the question then is how much structure should a (minimal) clause have? Grammar formalisms can differ significantly on this issue. ...

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We present an empirical study of the applicability of Probabilistic Lexicalized Tree Insertion Grammars (PLTIG), a lexicalized counterpart to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic naturallanguage processing. Comparing the performance of PLTIGs with non-hierarchical N-gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeling capability comparable to N-grams, and improved parsing performance over its nonlexicalized counterpart. Furthermore, training of PLTIGs displays faster convergence than PCFGs. ...

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