A new type of distance metric and its use for clustering

Gu, Xiaowei; Angelov, Plamen; Kangin, Dmitry; Prı́ncipe, José C.

doi:10.1007/s12530-017-9195-7

Cited by 28 publications

(16 citation statements)

References 25 publications

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“…Thus, the next step is to measure the distance between vectors. Then, 2) To obtain the proximity between objects, we combined two measures, namely, Euclidean and cosine, as these combined measures were proven to outperform other measures in clustering problems [28] and empirically proven also to obtain better results in this study. Because the cosine measure represents similarity and the Euclidean measure represents the distance between objects, we turn the Euclidean distance measure into a similarity measure by using the following adequacy:…”

Section: A Partitional Clustering Representationmentioning

confidence: 87%

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

et al. 2020

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The automatic text summarization (ATS) task consists in automatically synthesizing a document to provide a condensed version of it. Creating a summary requires not only selecting the main topics of the sentences but also identifying the key relationships between these topics. Related works rank text units (mainly sentences) to select those that could form the summary. However, the resulting summaries may not include all the topics covered in the source text because important information may have been discarded. In addition, the semantic structure of documents has been barely explored in this field. Thus, this study proposes a new method for the ATS task that takes advantage of semantic information to improve keyword detection. This proposed method increases not only the coverage by clustering the sentences to identify the main topics in the source document but also the precision by detecting the keywords in the clusters. The experimental results of this work indicate that the proposed method outperformed previous methods with a standard collection.

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Section: A Partitional Clustering Representationmentioning

confidence: 87%

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

et al. 2020

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“…We have used the Euclidean distance because it is currently the most frequently used metric space for the established clustering algorithms [30].…”

Section: Incremental Dbscan Clusteringmentioning

confidence: 99%

AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

Chefrour¹,

Souici-Meslati²

2019

IJCAI

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Density-based spatial clustering of applications with noise (DBSCAN) is a fundamental algorithm for density-based clustering. It can discover clusters of arbitrary shapes and sizes from a large amount of data, which contains noise and outliers. However, it fails to treat large datasets, outperform when new objects are inserted into the existing database, remove noise points or outliers totally and handle the local density variation that exists within the cluster. So, a good clustering method should allow a significant density modification within the cluster and should learn dynamics and large databases. In this paper, an enhancement of the DBSCAN algorithm is proposed based on incremental clustering called AMF-IDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMF-IDBSCAN algorithm uses a canopy clustering algorithm for pre-clustering the data sets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for post-clustering to reduce the number of outliers by replacing noises by chosen medians. Experiments with AMF-IDBSCAN are performed on the University of California Irvine (UCI) repository UCI data sets. The results show that our algorithm performs better than DBSCAN, IDBSCAN, and DMDBSCAN. Povzetek: V članku je predstavljen nov algoritem AMF-IDBSCAN, izboljšana različica DBSCAN, ki uporablja grozdenje krošenj za zmanjšanje obsega podatkov in tehnike AMF za odpravo hrupa.

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“…Based on these prototypes, the corresponding fuzzy rules are generated. Because of the very high dimensionality of the feature vectors, we use cosine dissimilarity as the distance measure, which is given below [30]: It has been demonstrated in [30] that the cosine dissimilarity between the original vectors of the global features is equivalent to the Euclidean distance between the vectors normalised by their norms (  x x x ) as described in equation 3. This is important because it facilitates the computational efficiency by allowing recursive calculation.…”

Section: Algorithmmentioning

confidence: 99%

Semi-supervised deep rule-based approach for image classification

Angelov

2018

Applied Soft Computing

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In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF…THEN… rules through the semi-supervised learning process in a selforganising and highly transparent manner. It supports online learning on a sample-by-sample basis or on a chunk-by-chunk basis. It is also able to perform classification on out-of-sample images. Moreover, the SSDRB classifier can learn new classes from unlabelled images in an active way becoming dynamically self-evolving. Numerical examples based on large-scale benchmark image sets demonstrate the strong performance of the proposed SSDRB classifier as well as its distinctive features compared with the "state-of-the-art" approaches.

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A new type of distance metric and its use for clustering

Cited by 28 publications

References 25 publications

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords

AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

Semi-supervised deep rule-based approach for image classification

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