Text Clustering using Distances Combination by Social Bees

2014 ◽  
Vol 4 (3) ◽  
pp. 34-53 ◽  
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Distance Measure ◽  
Text Clustering ◽  
Distance Measures ◽  
Cluster Number ◽  
3D Navigation ◽  
Social Bees ◽  
Clustering Problem ◽  
Novel Approach ◽  
Automatic Text Classification ◽  
N Gram

Recently, the researchers proved that 90% of the information existed on the web, were presented in unstructured format (text free). The automatic text classification (clustering), has become a crucial challenge in the computer science community, where Most of the classical techniques, have known different problems in terms of time execution, multiplicity of data (marketing, biology, economics), and the initialization of cluster number. Nowadays, the bio-inspired paradigm, has known a genuine success in several sectors and particularly in the world of data-mining. The content of our work, is a novel approach called distances combination by social bees (DC-SB) for text clustering, composed of four steps: Pre-processing using different methods of texts representation (bag of words and n-gram characters) and the weighting TF-IDF, for the construction of the vectors; Bees' artificial life, the authors have imitated the functioning of social bees using three artificial worker bees(cleaner, guardian and forager) where each one of them is characterized by a distance measure different to others generated from the artificial queen (centroid) of the cluster (hive); Clustering using the concept of filtering where each filter is controlled by an artificial worker, and a document must pass three different obstacles to be added to the cluster. For the experiments they use the benchmark Reuters 21578 and a variety of validation tools (execution time f-measure and entropy) with a variation of parameters (threshold, distance measures combination and texts representation). The authors have compared their results with the performances of other methods existed in literature (Cellular Automata 2D, Artificial Immune System (AIS) and Artificial Social Spiders (ASS)), the conclusion obtained prove that the approach can solve the text clustering problem; finally, the visualization step, which provides a 3D navigation of the results obtained by the mean of a global and detailed view of the hive and the apiary, using the functionality of zooming and rotation.

Intensity-based Image Registration with a Guaranteed One-to-one Point Match

2004 ◽  
Vol 43 (04) ◽  
pp. 327-330 ◽  
Author(s):  
J. Modersitzki ◽  
B. Fischer
Keyword(s):  
General Framework ◽  
Distance Measure ◽  
Distance Measures ◽  
The Novel ◽  
Clinical Environment ◽  
Post Intervention ◽  
Novel Approach ◽  
The First Variation ◽  
Registration Scheme ◽  
Objective Functional

Summary Objectives: In this paper, we propose a novel registration technique, which combines the concepts of landmark and automatic, non-rigid intensity-based approaches. A general framework, which might be used for many different registration problems is presented. The novel approach enables the incorporation of different distance measures as well as different smoothers. Methods: The proposed scheme minimizes a regular-ized distance measure subject to some interpolation constraints. The desired deformation is computed iteratively using an Euler-scheme for the first variation of the chosen objective functional. Results: A fast and robust numerical scheme for the computation of the wanted minimizer is developed, implemented, and applied to various registration tasks. This includes the registration of pre- and post-intervention images of the human eye. Conclusions: A novel framework for a parameter-free, non-rigid registration scheme which allows for the additional incorporation of user-defined landmarks is proposed. It enhances the reliability of conventional approaches considerably and thereby their acceptability by practitioners in a clinical environment.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

scholarly journals A Dynamic Distance Measure of Picture Fuzzy Sets and Its Application

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 436
Author(s):  
Ruirui Zhao ◽  
Minxia Luo ◽  
Shenggang Li
Keyword(s):  
Fuzzy Sets ◽  
Distance Measure ◽  
Distance Measures ◽  
Numerical Comparison ◽  
Mathematical Tool ◽  
Practical Applications ◽  
Fuzzy Point ◽  
Point Operator ◽  
The Difference ◽  
Picture Fuzzy Sets

Picture fuzzy sets, which are the extension of intuitionistic fuzzy sets, can deal with inconsistent information better in practical applications. A distance measure is an important mathematical tool to calculate the difference degree between picture fuzzy sets. Although some distance measures of picture fuzzy sets have been constructed, there are some unreasonable and counterintuitive cases. The main reason is that the existing distance measures do not or seldom consider the refusal degree of picture fuzzy sets. In order to solve these unreasonable and counterintuitive cases, in this paper, we propose a dynamic distance measure of picture fuzzy sets based on a picture fuzzy point operator. Through a numerical comparison and multi-criteria decision-making problems, we show that the proposed distance measure is reasonable and effective.

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

Linkage analysis using geographical proximity: a test of the efficacy of distance measures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shumpei Haginoya ◽  
Aiko Hanayama ◽  
Tamae Koike
Keyword(s):  
Environmental Factors ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Geographical Proximity ◽  
Discrimination Accuracy ◽  
Content Type ◽  
Shortest Route ◽  
The Impact

Purpose The purpose of this paper was to compare the accuracy of linking crimes using geographical proximity between three distance measures: Euclidean (distance measured by the length of a straight line between two locations), Manhattan (distance obtained by summing north-south distance and east-west distance) and the shortest route distances. Design/methodology/approach A total of 194 cases committed by 97 serial residential burglars in Aomori Prefecture in Japan between 2004 and 2015 were used in the present study. The Mann–Whitney U test was used to compare linked (two offenses committed by the same offender) and unlinked (two offenses committed by different offenders) pairs for each distance measure. Discrimination accuracy between linked and unlinked crime pairs was evaluated using area under the receiver operating characteristic curve (AUC). Findings The Mann–Whitney U test showed that the distances of the linked pairs were significantly shorter than those of the unlinked pairs for all distance measures. Comparison of the AUCs showed that the shortest route distance achieved significantly higher accuracy compared with the Euclidean distance, whereas there was no significant difference between the Euclidean and the Manhattan distance or between the Manhattan and the shortest route distance. These findings give partial support to the idea that distance measures taking the impact of environmental factors into consideration might be able to identify a crime series more accurately than Euclidean distances. Research limitations/implications Although the results suggested a difference between the Euclidean and the shortest route distance, it was small, and all distance measures resulted in outstanding AUC values, probably because of the ceiling effects. Further investigation that makes the same comparison in a narrower area is needed to avoid this potential inflation of discrimination accuracy. Practical implications The shortest route distance might contribute to improving the accuracy of crime linkage based on geographical proximity. However, further investigation is needed to recommend using the shortest route distance in practice. Given that the targeted area in the present study was relatively large, the findings may contribute especially to improve the accuracy of proactive comparative case analysis for estimating the whole picture of the distribution of serial crimes in the region by selecting more effective distance measure. Social implications Implications to improve the accuracy in linking crimes may contribute to assisting crime investigations and the earlier arrest of offenders. Originality/value The results of the present study provide an initial indication of the efficacy of using distance measures taking environmental factors into account.

scholarly journals Graph diffusion distance: Properties and efficient computation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249624
Author(s):  
C. B. Scott ◽  
Eric Mjolsness
Keyword(s):  
Distance Measure ◽  
Fixed Time ◽  
Graph Laplacian ◽  
Upper Bounds ◽  
Distance Measures ◽  
Distance Metrics ◽  
Graph Products ◽  
Efficient Computation ◽  
New Family ◽  
Sparsity Constraints

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts find a map minimizing the discrepancy between two graph Laplacian exponential matrices, under norm-preserving and sparsity constraints. Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians. The objective function of this optimization is multimodal and has discontinuous slope, and is hence difficult for univariate optimizers to solve. We demonstrate a novel procedure for efficiently calculating these optima for two of our distance measure variants. We present numerical experiments demonstrating that (a) upper bounds of our distance metrics can be used to distinguish between lineages of related graphs; (b) our procedure is faster at finding the required optima, by as much as a factor of 103; and (c) the upper bounds satisfy the triangle inequality exactly under some assumptions and approximately under others. We also derive an upper bound for the distance between two graph products, in terms of the distance between the two pairs of factors. Additionally, we present several possible applications, including the construction of infinite “graph limits” by means of Cauchy sequences of graphs related to one another by our distance measure.

scholarly journals INDUCED AND LOGARITHMIC DISTANCES WITH MULTI-REGION AGGREGATION OPERATORS

2019 ◽  
Vol 0 (0) ◽  
pp. 1-29 ◽  
Author(s):  
Víctor G. Alfaro-García ◽  
José M. Merigó ◽  
Leobardo Plata-Pérez ◽  
Gerardo G. Alfaro-Calderón ◽  
Anna M. Gil-Lafuente
Keyword(s):  
Distance Measure ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Averaging Operators ◽  
Heterogeneous Information ◽  
Weighting Vector ◽  
Choquet Integrals ◽  
World Information ◽  
Logarithmic Distance ◽  
Region Aggregation

This paper introduces the induced ordered weighted logarithmic averaging IOWLAD and multiregion induced ordered weighted logarithmic averaging MR-IOWLAD operators. The distinctive characteristic of these operators lies in the notion of distance measures combined with the complex reordering mechanism of inducing variables and the properties of the logarithmic averaging operators. The main advantage of MR-IOWLAD operators is their design, which is specifically thought to aid in decision-making when a set of diverse regions with different properties must be considered. Moreover, the induced weighting vector and the distance measure mechanisms of the operator allow for the wider modeling of problems, including heterogeneous information and the complex attitudinal character of experts, when aiming for an ideal scenario. Along with analyzing the main properties of the IOWLAD operators, their families and specific cases, we also introduce some extensions, such as the induced generalized ordered weighted averaging IGOWLAD operator and Choquet integrals. We present the induced Choquet logarithmic distance averaging ICLD operator and the generalized induced Choquet logarithmic distance averaging IGCLD operator. Finally, an illustrative example is proposed, including real-world information retrieved from the United Nations World Statistics for global regions.

Techniques for Fast Screening of 3D Heterogeneous Shale Barrier Configurations and Their Impacts on SAGD Chamber Development

SPE Journal ◽  
2021 ◽  
pp. 1-25
Author(s):  
Chang Gao ◽  
Juliana Y. Leung
Keyword(s):  
Distance Measure ◽  
Flow Simulation ◽  
Training Data ◽  
Distance Measures ◽  
Data Driven ◽  
Data Set ◽  
Flow Simulations ◽  
Steam Chamber ◽  
Reservoir Models ◽  
Tracking Model

Summary The steam-assisted gravity drainage (SAGD) recovery process is strongly impacted by the spatial distributions of heterogeneous shale barriers. Though detailed compositional flow simulators are available for SAGD recovery performance evaluation, the simulation process is usually quite computationally demanding, rendering their use over a large number of reservoir models for assessing the impacts of heterogeneity (uncertainties) to be impractical. In recent years, data-driven proxies have been widely proposed to reduce the computational effort; nevertheless, the proxy must be trained using a large data set consisting of many flow simulation cases that are ideally spanning the model parameter spaces. The question remains: is there a more efficient way to screen a large number of heterogeneous SAGD models? Such techniques could help to construct a training data set with less redundancy; they can also be used to quickly identify a subset of heterogeneous models for detailed flow simulation. In this work, we formulated two particular distance measures, flow-based and static-based, to quantify the similarity among a set of 3D heterogeneous SAGD models. First, to formulate the flow-based distance measure, a physics-basedparticle-tracking model is used: Darcy’s law and energy balance are integrated to mimic the steam chamber expansion process; steam particles that are located at the edge of the chamber would release their energy to the surrounding cold bitumen, while detailed fluid displacements are not explicitly simulated. The steam chamber evolution is modeled, and a flow-based distance between two given reservoir models is defined as the difference in their chamber sizes over time. Second, to formulate the static-based distance, the Hausdorff distance (Hausdorff 1914) is used: it is often used in image processing to compare two images according to their corresponding spatial arrangement and shapes of various objects. A suite of 3D models is constructed using representative petrophysical properties and operating constraints extracted from several pads in Suncor Energy’s Firebag project. The computed distance measures are used to partition the models into different groups. To establish a baseline for comparison, flow simulations are performed on these models to predict the actual chamber evolution and production profiles. The grouping results according to the proposed flow- and static-based distance measures match reasonably well to those obtained from detailed flow simulations. Significant improvement in computational efficiency is achieved with the proposed techniques. They can be used to efficiently screen a large number of reservoir models and facilitate the clustering of these models into groups with distinct shale heterogeneity characteristics. It presents a significant potential to be integrated with other data-driven approaches for reducing the computational load typically associated with detailed flow simulations involving multiple heterogeneous reservoir realizations.

Improved Lymphocyte Image Segmentation Using Near Sets for ALL Detection

2016 ◽  
pp. 317-334
Author(s):  
Shiwangi Chhawchharia ◽  
Subrajeet Mohapatra ◽  
Gadadhar Sahoo
Keyword(s):  
Image Segmentation ◽  
Distance Measure ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Near Sets ◽  
Hematological Disorders ◽  
Clustering Problem ◽  
Clustering Approach ◽  
Nearness Measure ◽  
Image Segmentation Algorithm

Light microscopic examination of peripheral blood smear is considered vital for diagnosis of various hematological disorders. The objective of this paper is to develop a fast, robust and simple framework for blood microscopic image segmentation which can assist in automated detection of hematological diseases i.e. acute lymphoblastic leukemia (ALL). A near set based clustering approach is followed for color based segmentation of lymphocyte blood image. Here, a novel distance measure using near sets has been introduced. This improved nearness distance measure has been used in a clustering framework for achieving accurate lymphocyte image segmentation. The nearness measure determines the degree to which two pixels resemble each other based on a defined probe function. It is essential as image segmentation is considered here as a colour based pixel clustering problem. Lymphocyte image segmentation algorithm developed here labels each pixel into nucleus, cytoplasm or background region based on the nearness measure.

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