# Measures Of Similarity And Dissimilarity In Data Mining Pdf

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- A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data
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- Fakultas Perikanan dan Ilmu Kelautan
- A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

## A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection Abstract: Anomaly detection AD use within the network intrusion detection field of research, or network intrusion AD NIAD , is dependent on the proper use of similarity and distance measures, but the measures used are often not documented in published research. As a result, while the body of NIAD research has grown extensively, knowledge of the utility of similarity and distance measures within the field has not grown correspondingly.

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Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category.

Interestingness measures for data mining: A survey. In data mining, ample techniques use distance measures to some extent. A small distance indicating a high degree of similarity and a large distance indicating a low degree of similarity. The state or fact of being similar or Similarity measures how much two objects are alike. Download Free PDF. On top of already mentioned distance measures, the distance between two distributions can be found using as well Kullback-Leibler or Jensen-Shannon divergence. Other Distance Measures.

Show all documents An Effective FCM Approach of Similarity and Dissimilarity Measures with -Cut Fuzzy set theory introduced by Zadeh [10] uses the concept of uncertainty in the definition of a set by removing the crisp boundary concept into a function of the degree of membership or non- membership [11]. Fuzzy logic using fuzzy set theory provides important tools for data mining and to determine the data quality and has been proven to have the ability to present uncertain data that contain vagueness, uncertainty and incompleteness [12]. This is especially observed if the databases are complex. Classifiers based on fuzzy set theory like the Fuzzy c- Means classifier FCM [13] has been studied with weighted measures such as the Euclidean measure, Mahalanobis measure or a diagonal Mahalanobis measure for solving mixed pixel problems in remote sensing images [14]. Earlier, other measures of similarity and dissimilarity measures such as the correlation, Canberra, Cosine distance, etc.

In book: Advances in Data Mining Knowledge Discovery and Applications; Chapter: 3; Editors: InTech Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining 73 dissimilarity measures.

## Fakultas Perikanan dan Ilmu Kelautan

Use of this Web site signifies your agreement to the terms and conditions. Special Issues. Contact Us. Change code. In most studies related to time series data mining, referred to the LCSS and Dynamic Time Warping DTW methods as the best and most usable for similarity measurement methods, but the LCSS is intrinsically designed to measure the similarity of two sequences of character, which later was developed for time series by defining and determining the similarity threshold.

Distance or similarity measures are essential in solving many pattern recognition problems such as classification and clustering. As the names suggest, a similarity measures how close two distributions are. For multivariate data complex summary methods are developed to answer this question. Distance , such as the Euclidean distance, is a dissimilarity measure and has some well-known properties: Common Properties of Dissimilarity Measures.

### A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

A large variety of real world applications, such as meteorology, geophysics and astrophysics, collect observations that can be represented as time series. Given a TSDB , most of time series mining efforts are made for the similarity matching problem. Time series data mining can be exploited from research areas dealing with signals, such as image processing. For example, image data can be converted to time series: from image color histograms Fig.

Most of unsupervised learning algorithms use a dissimilarity function to measures similarity between the objects within the dataset. However, traditionally dissimilarity functions did not design and fail to treat all spatial attributes of region or just solve partial kinds of region since incomplete representation of structural of region and other spatial information contained within the region datasets. In this research, we modified polygonal dissimilarity function PDF that comprehensively integrates both the spatial and the non-spatial attributes of a polygon to specifically consider the density and distribution that exist within the region datasets and work well to regular region, but not for irregular region.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Shirkhorshidi and S. Shirkhorshidi , S.

#### 1. Introduction

Due to the key role of these measures, different similarity functions for categorical data have been proposed Boriah et al. Similarity and Dissimilarity are important because they are used by a number of data mining techniques, such as clustering, nearest neighbour classification, and anomaly detection. As with cosine, this is useful under the same data conditions and is well suited for market-basket data. In everyday life it usually means some degree of closeness of two physical objects or ideas, while the term metric is often used as a standard for a measurement. Data clustering is an important part of data mining.

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Он с трудом сдержал улыбку. - Только лишь мошонка. Офицер гордо кивнул: - Да.