scholarly journals Relative Hausdorff distance for network analysis

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Sinan G. Aksoy ◽  
Kathleen E. Nowak ◽  
Emilie Purvine ◽  
Stephen J. Young
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Similarity Measure ◽  
Hausdorff Distance ◽  
Data Science ◽  
Edit Distance ◽  
Similarity Measures ◽  
Graph Edit Distance ◽  
Computationally Intensive

Abstract Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs. In this work we study the effectiveness of RH distance as a tool for detecting anomalies in time-evolving graph sequences. We apply RH to cyber data with given red team events, as well to synthetically generated sequences of graphs with planted attacks. In our experiments, the performance of RH distance is at times comparable, and sometimes superior, to graph edit distance in detecting anomalous phenomena. Our results suggest that in appropriate contexts, RH distance has advantages over more computationally intensive similarity measures.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals Redefining the Graph Edit Distance

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Triangle Inequality ◽  
Edit Distance ◽  
Minimum Amount ◽  
Graph Edit Distance ◽  
Edit Operation ◽  
Attributed Graphs ◽  
Distance Properties

AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.

scholarly journals Possibilistic Similarity Measures for Data Science and Machine Learning Applications

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49198-49211
Author(s):  
Amal Charfi ◽  
Sonda Ammar Bouhamed ◽  
Eloi Bosse ◽  
Imene Khanfir Kallel ◽  
Wassim Bouchaala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Similarity Measures ◽  
Machine Learning Applications

scholarly journals Learning similarity measures from data

2019 ◽  
Vol 9 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Agnar Aamodt ◽  
Kerstin Bach ◽  
Helge Langseth
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Learning System ◽  
Case Based Reasoning ◽  
Training Time ◽  
Domain Experts ◽  
Trained Classifier ◽  
Clustering Data

Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

scholarly journals Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure

2019 ◽  
Vol 59 (4) ◽  
pp. 1410-1421 ◽  
Author(s):  
Carlos Garcia-Hernandez ◽  
Alberto Fernández ◽  
Francesc Serratosa
Keyword(s):  
Virtual Screening ◽  
Similarity Measure ◽  
Edit Distance ◽  
Molecular Similarity ◽  
Graph Edit Distance

scholarly journals Upper Bounding Graph Edit Distance Based on Rings and Machine Learning

2021 ◽  
pp. 2151008 ◽  
Author(s):  
David B. Blumenthal ◽  
Johann Gamper ◽  
Sébastien Bougleux ◽  
Luc Brun
Keyword(s):  
Machine Learning ◽  
Edit Distance ◽  
Distance Measure ◽  
Graph Matching ◽  
Machine Learning Techniques ◽  
Graph Edit Distance ◽  
Local Structures ◽  
Learning Techniques ◽  
Inexact Graph Matching ◽  
Node Labels

The graph edit distance (GED) is a flexible distance measure which is widely used for inexact graph matching. Since its exact computation is [Formula: see text]-hard, heuristics are used in practice. A popular approach is to obtain upper bounds for GED via transformations to the linear sum assignment problem with error-correction (LSAPE). Typically, local structures and distances between them are employed for carrying out this transformation, but recently also machine learning techniques have been used. In this paper, we formally define a unifying framework LSAPE-GED for transformations from GED to LSAPE. We also introduce rings, a new kind of local structures designed for graphs where most information resides in the topology rather than in the node labels. Furthermore, we propose two new ring-based heuristics RING and RING-ML, which instantiate LSAPE-GED using the traditional and the machine learning-based approach for transforming GED to LSAPE, respectively. Extensive experiments show that using rings for upper bounding GED significantly improves the state of the art on datasets where most information resides in the graphs’ topologies. This closes the gap between fast but rather inaccurate LSAPE-based heuristics and more accurate but significantly slower GED algorithms based on local search.

scholarly journals Modified Cosine Similarity Measure based Data Classification in Data Mining

2020 ◽  
Vol 9 (5) ◽  
pp. 649-654
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Similarity Measure ◽  
Dominant Role ◽  
Similarity Measures ◽  
Data Classification ◽  
Cosine Similarity ◽  
Machine Learning Techniques ◽  
Text Data ◽  
Cosine Similarity Measure

Text data analytics became an integral part of World Wide Web data management and Internet based applications rapidly growing all over the world. E-commerce applications are growing exponentially in the business field and the competitors in the E-commerce are gradually increasing many machine learning techniques for predicting business related operations with the aim of increasing the product sales to the greater extent. Usage of similarity measures is inevitable in modern day to day real applications. Cosine similarity plays a dominant role in text data mining applications such as text classification, clustering, querying, and searching and so on. A modified clustering based cosine similarity measure called MCS is proposed in this paper for data classification. The proposed method is experimentally verified by employing many UCI machine learning datasets involving categorical attributes. The proposed method is superior in producing more accurate classification results in majority of experiments conducted on the UCI machine learning datasets.

An effective similarity measure based on kernel spectral method for complex networks

2019 ◽  
Vol 30 (07) ◽  
pp. 1940005
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Shenshen Bai ◽  
Shiyu Fang ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Spectral Method ◽  
Similarity Measure ◽  
Link Prediction ◽  
Structural Information ◽  
Critical Role ◽  
Similarity Measures ◽  
Global Information ◽  
Kernel Matrix ◽  
Node Similarity

Node similarity measure is a special important task in complex network analysis and plays a critical role in a multitude of applications, such as link prediction, community detection, and recommender systems. In this study, we are interested in link-based similarity measures, which only concern the structural information of networks when estimating node similarity. A new algorithm is proposed by adopting the idea of kernel spectral method to quantify the similarity of nodes. When computing the kernel matrix, the proposed algorithm makes use of local structural information, but it takes advantage of global information when constructing the feature matrix. Thence, the proposed algorithm could better capture potential relationships between nodes. To show the superiority of our algorithm over others, we conduct experiments on 10 real-world networks. Experimental results demonstrate that our algorithm yields more reasonable results and better performance of accuracy than baselines.

scholarly journals Ensemble Creation using Fuzzy Similarity Measures and Feature Subset Evaluators

2021 ◽  
Author(s):  
Valerie Cross ◽  
Michael Zmuda
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Learning Process ◽  
Similarity Measures ◽  
Training Data ◽  
Feature Subset ◽  
Data Set ◽  
Fuzzy Similarity

Current machine learning research is addressing the problem that occurs when the data set includes numerous features but the number of training data is small. Microarray data, for example, typically has a very large number of features, the genes, as compared to the number of training data examples, the patients. An important research problem is to develop techniques to effectively reduce the number of features by selecting the best set of features for use in a machine learning process, referred to as the feature selection problem. Another means of addressing high dimensional data is the use of an ensemble of base classifiers. Ensembles have been shown to improve the predictive performance of a single model by training multiple models and combining their predictions. This paper examines combining an enhancement of the random subspace model of feature selection using fuzzy set similarity measures with different measures of evaluating feature subsets in the construction of an ensemble classifier. Experimental results show that in most cases a fuzzy set similarity measure paired with a feature subset evaluator outperforms the corresponding fuzzy similarity measure by itself and the learning process only needs to occur on typically about half the number of base classifiers since the features subset evaluator eliminates those feature subsets of low quality from use in the ensemble. In general, the fuzzy consistency index is the better performing feature subset evaluator, and inclusion maximum is the better performing fuzzy similarity measure.

scholarly journals Hierarchy-based semantic embeddings for single-valued & multi-valued categorical variables

2021 ◽  
Author(s):  
Summaya Mumtaz ◽  
Martin Giese
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
Domain Knowledge ◽  
Similarity Measures ◽  
Training Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Use Case ◽  
Data Types ◽  
Semantic Similarity Measure

AbstractIn low-resource domains, it is challenging to achieve good performance using existing machine learning methods due to a lack of training data and mixed data types (numeric and categorical). In particular, categorical variables with high cardinality pose a challenge to machine learning tasks such as classification and regression because training requires sufficiently many data points for the possible values of each variable. Since interpolation is not possible, nothing can be learned for values not seen in the training set. This paper presents a method that uses prior knowledge of the application domain to support machine learning in cases with insufficient data. We propose to address this challenge by using embeddings for categorical variables that are based on an explicit representation of domain knowledge (KR), namely a hierarchy of concepts. Our approach is to 1. define a semantic similarity measure between categories, based on the hierarchy—we propose a purely hierarchy-based measure, but other similarity measures from the literature can be used—and 2. use that similarity measure to define a modified one-hot encoding. We propose two embedding schemes for single-valued and multi-valued categorical data. We perform experiments on three different use cases. We first compare existing similarity approaches with our approach on a word pair similarity use case. This is followed by creating word embeddings using different similarity approaches. A comparison with existing methods such as Google, Word2Vec and GloVe embeddings on several benchmarks shows better performance on concept categorisation tasks when using knowledge-based embeddings. The third use case uses a medical dataset to compare the performance of semantic-based embeddings and standard binary encodings. Significant improvement in performance of the downstream classification tasks is achieved by using semantic information.

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