Visual Identification of Inconsistency in Pattern (original) (raw)

Finding Patterns in Visualized Data by Adding Redundant Visual Information

2022

We present "PATRED", a technique that uses the addition of redundant information to facilitate the detection of specific, generally described patterns in line-charts during the visual exploration of the charts. We compared different versions of this technique, that differed in the way redundancy was added, using nine distance metrics (such as Euclidean, Pearson, Mutual Information and Jaccard) with judgments from data scientists which served as the "ground truth". Results were analyzed with correlations (R 2), F1 scores and Mutual Information with the average ranking by the data scientists. Some distance metrics consistently benefit from the addition of redundant information, while others are only enhanced for specific types of data perturbations. The results demonstrate the value of adding redundancy to improve the identification of patterns in time-series data during visual exploration.

Conceptualization Approach for Inconsistency Data by Means of Mining Proceeding Methods

Most business processes change over time, and contemporary practices in the mining process, however, they are in a constant state of analysis processes. A process that can change suddenly or gradually. It is drifting in the league, or one of its kind (for example, the new law) might be (for example, due to seasonal effects) is. Perform the procedure, and it is necessary to understand the intentions of the concept of practical presentations. This paper presents the process of transformation and the process to find out the changed components. The proposed measures describe the relationship between different features. It is to find the differences in the characteristics of the population.

Visualizations of binary data: A comparative evaluation

International Journal of Human-Computer Studies, 2003

Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using nongraphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available. r

A unified and flexible framework for comparing simple and complex patterns

Knowledge Discovery in …

J.-F. Boulicaut et al. (Eds.): PKDD 2004, LNAI 3202, pp. 496–499, 2004. © Springer-Verlag Berlin Heidelberg 2004 ... A Unified and Flexible Framework for Comparing Simple and Complex Patterns* ... Ilaria Bartolini1, Paolo Ciaccia1, Irene Ntoutsi2, Marco Patella1, and Yannis ...

Displaying Relationship Anomalies

2018

Naive measures of association between variables, such as linear correlation, are primarily sensitive to gross relationships, those patterns that are easy to detect, see, and describe. In prior chapters we examined measures that go beyond such naivete and are able to detect more subtle dependencies between variables, in other words, anomalies in otherwise uncomplicated relationships. But what if we want a visual representation of the pattern that connects them? In this chapter we present several ways of doing this.

An Improved Technique for the Removal and Replacement of the Inconsistencies in Numeric Dataset

African Journal of Computing & ICT, 2015

The task of ensuring the removal of anomalies in an unclean numeric dataset, with a view to putting the data in a suitable format for exploration purposes is a major phase in the data mining process. In the process of exploring an unclean numeric dataset to unveil their useful patterns or structure, a thorough pre-processing task is inevitable in order to achieve a noise-free dataset. Poor quality data can be misleading if analysed or used to build models, hence, there is need to remove discrepancies that may be present in the data prior to exploring them. In this paper, a cleaning algorithm is proposed and implemented in order to remove the inconsistencies in a numeric dataset. The implementation of the proposed algorithm uses the Java language and the resulting outputs reveal the efficiency of the proposed approach. In order to evaluate the effectiveness of the proposed algorithm, it is compared to one of the existing methods based on some metrics. The comparisons show that, the proposed technique is efficient and can be used as an alternative technique for the removal of outliers in numeric data. This approach is also found to be reliable as it consistently gives an accurate output that is free of outliers.

Patterns for Visualization Evaluation

2012

ABSTRACT We propose a patterns-based approach to evaluating data visualization: a set of general and reusable solutions to commonly occurring problems in evaluating tools, techniques, and systems for visual sensemaking. Patterns have had significant impact in a wide array of disciplines, particularly software engineering, and we believe that they provide a powerful lens for looking at visualization evaluation by offering practical, tried-and-tested tips and tricks that can be adopted immediately.

Detection of Discordant Observations and Visualization of Data

2011

Discordant Observations are special values or extraordinary cases in the available data which deviate so much from other observations so as to arouse suspicions that they were generated by a different mechanism. They can be used to identify special or extraordinary or fraudulent cases in day to day t ransactions. Preprocessing can be used to identify the noise in the data and removal of such noise improves data quality. Discordant Observations are also called Anomalies or Outliers. Anomaly Detection can be used for Traffic Analysis, Credit Card Fraud Detection. We applied Anomaly Detection to Traffic data set for identifying the anomaly traffic stations on the highway. Detected stations represent abnormalities in the traffic sensors data. This information is used by us to identify the faulty traffic sensors located at the highway stations. Two dimensional visualization of the outliers has been provided which can be used for analyzing the data in an efficient manner. Traffic Manageme...