# Measures Of Similarity And Dissimilarity In Data Mining Pdf

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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.*

- Fakultas Perikanan dan Ilmu Kelautan
- Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining
- Fakultas Perikanan dan Ilmu Kelautan
- measures of similarity and dissimilarity

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## Fakultas Perikanan dan Ilmu Kelautan

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. NIAD research covers a myriad of domains and employs a diverse array of techniques from simple k-means clustering through advanced multiagent distributed AD systems.

This review presents an overview of the use of similarity and distance measures within NIAD research. The analysis provides a theoretical background in distance measures and a discussion of various types of distance measures and their uses. Exemplary uses of distance measures in published research are presented, as is the overall state of the distance measure rigor in the field.

Finally, areas that require further focus on improving the distance measure rigor in the NIAD field are presented. Article :. Date of Publication: 11 July DOI: Need Help?

## Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

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 distance that satisfies these properties is called a metric.

In data mining, ample techniques use distance measures to some extent. based clustering similarity or dissimilarity (distance) measures are the core .pdf. Zhang Z, Huang K, Tan T. Comparison of similarity.

## Fakultas Perikanan dan Ilmu Kelautan

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*Interestingness measures for data mining: A survey.*

### measures of similarity and dissimilarity

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. NIAD research covers a myriad of domains and employs a diverse array of techniques from simple k-means clustering through advanced multiagent distributed AD systems.

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 this work, these measures were studied with FCM classifier.

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. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated. In a Data Mining sense, the similarity measure is a distance with dimensions describing object features.

Due to the key role of these measures, different similarity functions for categorical data have been proposed (Boriah et al., ). Similarity and Dissimilarity are.

#### ICHWANUL MUSLIM KARO KARO

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. Time series are essentially high-dimensional data [ 17 ]. Mining high-dimensional involves addressing a range of challenges, among them: i the curse of dimensionality [ 1 ], and ii the meaningfulness of the similarity measure in the high-dimensional space. An important task to enhance performances on time series is the reduction of their dimensionality, that must preserve the main characteristics, and reflects the original dis similarity of such data this effect will be referred to as lower bounding [ 11 ].

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. We represent a polygon as a set of intrinsic spatial attributes by slice vertices and structural region, extrinsic spatial attributes, and non-spatial attributes. Spatial clustering by CLARANS with modified PDF using two characteristically different sets of data, a regular geometric shapes dummy region and b irregular geometric shapes, Jakarta crime as case study on spatial clustering. Completely spatial information has above fifty percents significances and best cluster result for all dataset. Informasi Dasar.

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