Outlier Detection Using Improved Genetic K-Means

Marghny, M. H.; Taloba, Ahmed I.

doi:10.2139/ssrn.2545143

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…This trend continues until converging the clusters so that all of the anomalous data is removed from the data set [27]. In [28], the authors applied a similar method to the Local Outlier Factor (LOF) algorithm in order to determine the degree of anomaly based on distance from centroid. In the first step, by improving the K-means algorithm using genetic algorithm, data set is clustered.…”

Section: Related Workmentioning

confidence: 99%

A new anomalous text detection approach using unsupervised methods

et al. 2020

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Increasing size of text data in databases requires appropriate classification and analysis in order to acquire knowledge and improve the quality of decision-making in organizations. The process of discovering the hidden patterns in the data set, called data mining, requires access to quality data in order to receive a valid response from the system. Detecting and removing anomalous data is one of the pre-processing steps and cleaning data in this process. Methods for anomalous data detection are generally classified into three groups including supervised, semi-supervised, and unsupervised. This research tried to offer an unsupervised approach for spotting the anomalous data in text collections. In the proposed method, a combination of two approaches (i.e., clustering-based and distance-based) is used for detecting anomaly in the text data. In order to evaluate the efficiency of the proposed approach, this method is applied on four labeled data sets. The accuracy of Na?ve Bayes classification algorithms and decision tree are compared before and after removal of anomalous data with the proposed method and some other methods such as Density-based spatial clustering of applications with noise (DBSCAN). Our proposed method shows that accuracy of more than 92.39% can be achieved. In general, the results revealed that in most cases the proposed method has a good performance.

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Section: Related Workmentioning

confidence: 99%

A new anomalous text detection approach using unsupervised methods

et al. 2020

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“…Also many advanced modifications have been proposed. But these methods has a primary limitation that each pixel is determined based on the similar decision rule without considering how much impulse-like each pixel is (Marghny and Taloba, 2014). Moreover the performance of these methods are poor for higher noise density (Sreedevi and Sherly, 2015).…”

Section: B Modified Robust Outlyingness Ratio (Mror)mentioning

confidence: 99%

Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means

Rajaguru¹,

Chakravarthy²

2020

Asian Pac J Cancer Prev

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Objective: The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic-computer aided detection (CAD) of microcalcification in digital mammography. Methods: In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism. Results: According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image. Conclusion: The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.

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“…H. Marghny [7]proposed an outlier detection algorithm using Improved Genetic K-means algorithm. This algorithm includes two stages: In first stage, improved genetic k-means algorithm is used for clustering and in second stages all the vectors which are far from their cluster centroids are removed iteratively.…”

Section: Related Workmentioning

confidence: 99%

Spatial data mining for finding nearest neighbor and outlier detection

2016

IJLTET

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A spatial database is a collection of records related to space. The space can be a geographic space, a human body or a VLSI chip [15]. In SDBMS, objects are defined in a geometrical shape such as points, lines and polygons. Spatial database offers spatial data types, data models and query languages to process the spatial data. The major components of spatial database include a data model, query languages, processing and optimization tools, and indices. Examples of spatial databases include weather climate data, river, farms, medical imaging etc. Spatial databases support spatial indexing, efficient algorithms for processing spatial operations, and domain specific rules for query optimization. Spatial databases can be used in medical, astrology, biology, and defense and in many more areas.A geographic information system (GIS) or geospatial information system is designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. In GIS there can be many problems that include finding the nearest neighbor or finding the outliers in a data set. Abstract-Spatial data mining is a process to extract interesting patterns related to space. Space can be geographic space, the universe, a VLSI design, a molecular structure, or a human body. With the proliferation in use of spatial databases the probability of getting outliers is also increased. These outliers can be noisy data or highly valuable information. If the noise exists in the database, the performance of data mining algorithm may be degraded [14]. Detection of outliers in spatial database can be area of research in various applications. Spatial databases can be used in location based services (e.g. Google maps) to find nearest neighbors. If there is a data point which is not nearer to other data points, then this data point is considered as outlier. In this paper, we have discussed various researches for finding nearest neighbor and outlier detection.

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Outlier Detection Using Improved Genetic K-Means

Cited by 9 publications

References 15 publications

A new anomalous text detection approach using unsupervised methods

A new anomalous text detection approach using unsupervised methods

Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means

Spatial data mining for finding nearest neighbor and outlier detection

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