Search results for "data structure"
showing 10 items of 441 documents
Sequential Mining Classification
2017
Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …
External labeling and algorithms for hierarchic networks
1988
Algorithms for generating internal data structures for networks are given. Data bases for networks can be partitioned hierarchically. Nodes of lower class networks may also be in the interior of a higher class arc which will be split if it is included into the final network. Naming is based either on nodes or on arcs.
Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping
2016
The choice of the proper resolution in landslide susceptibility mapping is a worth considering issue. If, on the one hand, a coarse spatial resolution may describe the terrain morphologic properties with low accuracy, on the other hand, at very fine resolutions, some of the DEM-derived morphometric factors may hold an excess of details. Moreover, the landslide inventory maps are represented throughout geospatial vector data structure, therefore a conversion procedure vector-to-raster is required.This work investigates the effects of raster resolution on the susceptibility mapping in conjunction with the use of different algorithms of vector-raster conversion. The Artificial Neural Network t…
Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning
2009
In recent years, LIDAR (light detection and ranging) sensors have been widely used to measure environmental parameters such as the structural characteristics of trees, crops and forests. Knowledge of the structural characteristics of plants has a high scientific value due to their influence in many biophysical processes including, photosynthesis, growth, CO2-sequestration and evapotranspiration, playing a key role in the exchange of matter and energy between plants and the atmosphere, and affecting terrestrial, above-ground, carbon storage. In this work, we report the use of a 2D LIDAR scanner in agriculture to obtain three-dimensional (3D) structural characteristics of plants. LIDAR allows…
Cluster-based active learning for compact image classification
2010
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer…
A distance metric on binary trees using lattice-theoretic measures
1990
A so called height function which is a strictly antitone supervaluation is defined on binary trees. Via lattice-theoretic results and using the height function, we can define a distance metric on binary trees of size n which can be computed in expected time O(n 3/2 )
Variable-order reference-free variant discovery with the Burrows-Wheeler Transform
2020
Abstract Background In [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory con…
Surface Reconstruction Based on a Descriptive Approach
2000
The design of complex surfaces is generally hard to achieve. A natural method consists in the subdivision of the global surface into basic surface elements. The different elements are independently designed and then assembled together to represent the final surface. This method requires a classification and a formal description of the basic elements. This chapter presents a general framework for surface description, based on a constructive tree approach. In this tree the leaves are surface primitives and the nodes are constructive operators.
ImageRover: A Content-Based Image Browser for the World Wide Web
1997
ImageRover is a search-by-image-content navigation tool for the World Wide Web (WWW). To gather images expediently, the image collection subsystem utilizes a distributed fleet of WWW robots running on different computers. The image robots gather information about the images they find, computing the appropriate image decompositions and indices, and store this extracted information in vector form for searches based on image content. At search time, users can iteratively guide the search through the selection of relevant examples. Search performance is made efficient through the use of an approximate, optimized k-d tree algorithm. The system employs a novel relevance feedback algorithm that se…
Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree
2021
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation p…