Christopher L. Carmichael’s research while affiliated with University of Manitoba and other places

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Publications (14)


Interactive Visual Analytics of Big Data
  • Chapter

January 2017

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46 Reads

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12 Citations

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Christopher L. Carmichael

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Patrick Johnstone

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[...]

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David Sonny Hung-Cheung Yuen

High volumes of a wide variety of data can be easily generated at a high velocity in many real-life applications. Implicitly embedded in these big data is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for either retrieving information about the data or mining the data to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. However, many of the existing visualizers were not designed to visualize these mined frequent sets. This book chapter presents an interactive next-generation visual analytic system. The system enables the management, visualization, and advanced analysis of the original big data and the frequent sets mined from the data.


Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns

September 2016

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77 Reads

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5 Citations

Lecture Notes in Computer Science

As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. As a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. This is when human-computer interaction (HCI) research meets data mining research. In particular, the popular HCI task of data and result visualization could help data miners to visualize the original data and to analyze the mined results (in the form of frequent patterns). In this paper, we present a few systems for data and visual analytics of frequent patterns, which integrate (i) data analytics and mining with (ii) data and result visualization.


Interactive Visual Analytics of Databases and Frequent Sets

October 2015

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18 Reads

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4 Citations

In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. Many of the existing visualizers were not designed to visualize these mined frequent sets. In this journal article, an interactive visual analytic system is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the management, visualization, and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.


Visually Contrast Two Collections of Frequent Patterns

December 2011

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10 Reads

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9 Citations

Frequent pattern mining searches for frequently occurring sets of items or events. While users are interested in finding these frequent patterns in most situations, they may want to compare and contrast the mined frequent patterns in some other situations. For example, store managers may want to find out how the collections of frequently purchased items changed from one season to another. Similarly, regional managers may want to compare the frequently purchased items between two different branches. These are some examples of looking for temporal and/or spatial changes between mined frequent patterns. A visual representation of these patterns would be more comprehensive to users than the long textual list returned by many existing frequent pattern mining algorithms. However, many existing visualizers were not designed to show frequent patterns, let alone show the differences between them. In this paper, we propose a visualization system called Contrast Viz that enables users to visualize the mined frequent patterns and their differences.


Visual Analytics of Social Networks: Mining and Visualizing Co-authorship Networks

July 2011

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44 Reads

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61 Citations

Lecture Notes in Computer Science

Co-authorship networks are examples of social networks, in which researchers are linked by their joint publications. Like many other instances of social networks, co-authorship networks contain rich sets of valuable data. In this paper, we propose a visual analytic tool, called SocialVis, to analyze and visualize these networks. In particular, SocialVis first applies frequent pattern mining to discover implicit, previously unknown and potential useful social information such as teams of multiple frequently collaborating researchers, their composition, and their collaboration frequency. SocialVis then uses a visual representation to present the mined social information so as to help users get a better understanding of the networks.


iVAS: An Interactive Visual Analytic System for Frequent Set Mining
  • Chapter
  • Full-text available

October 2010

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741 Reads

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5 Citations

Nowadays, various data, text, and web mining applications can easily generate large volumes of data. Embedded within these data is previously unknown and potential useful knowledge such as frequently occurring sets of items, merchandise, or events. Hence, numerous algorithms have been proposed for finding these frequent sets, which are usually presented in a lengthy textual list. However, "a picture is worth a thousand words". The use of visual representations can enhance user understanding of the inherent relations among the frequent sets. Although a few visualizers have been developed, most of them were not designed for visualizing the mined frequent sets. In this chapter, an interactive visual analytic system called iVAS is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the visualization and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.

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Exploring Social Networks: A Frequent Pattern Visualization Approach

August 2010

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47 Reads

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59 Citations

Social network analysis and mining aims to search for implicit, previously unknown, and potentially useful relational information (e.g., social relationship) from social networks. A visual representation of the networks helps users to gain insights about the useful information mined from the networks. Many existing visualizers represent social networks as graphs. While the graphs depict pairwise connections between two social entities, they may not show the connection strength or the multi-entity relationship (e.g., coauthorship and collaboration frequency). In this paper, we propose a visualizer called SocialViz for providing users with frequency information on social relationship among multiple entities in the networks. SocialViz can serve as a standalone visualization tool, or as a complement to existing visualizers, for exploring social networks.


Figure 1: DNA copy number amplifications in chromosome-17, resolution 393. χ = (Xij ), Xij ∈ {0, 1}. Each row represents one sample of the amplification pattern for a patient and each column represents one of the chromosome bands. 
Figure 1: The researcher's point of view when designing objective interestingness measures (left, where he coincides with the practitioner) and subjective interestingness measures (right). 
Figure 1: Schematic of a 1-dimensional data set of points together with two example items. Smoothed point distributions for the overall data set as well as for points having the individual items are shown. It can be seen that the distribution of points with item 1 shows two peaks that are much higher than what would be expected based on the overall distribution (strong pattern exists). The distribution of points with item 2 follows the overall distribution much more closely (no strong pattern exists). 
Figure 2: A data mining algorithm implementing our framework can be viewed as an interface moderating the communication between the data miner (Alice) and Bob (the data), trying to help convey the data from Alice to Bob as efficiently as possible. It takes into account what Bob already knows (or thinks to know), as well as the syntactic form of the patterns he believes the data may contain. This figure illustrates that for the running example on mining interesting tiles. 
Figure 2: Example of a vector-item pattern that cannot be found by a two-step approach of clustering and enrichment analysis. Two-dimenensional vector information determines the location of objects in the plane. Data points with the items set are show as solid black and others as empty circles. 

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CloseViz: Visualizing useful patterns

July 2010

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259 Reads

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12 Citations

Numerous algorithms have been proposed since the introduction of the research problem of frequent pattern mining. Such a research problem has played an essential role in many knowledge discovery and data mining (KDD) tasks. Most of the proposed frequent pattern mining algorithms return the mined results in the form of textual lists that contain frequent patterns showing those frequently occurring sets of items. As "a picture is worth a thousand words", the use of visual representation can enhance the user understanding of the inherent relations in a collection of frequent patterns. Although a few visualizers have been developed to visualize the raw data or the results for some data mining tasks, most of these visualizers were not designed for visualizing frequent patterns. For those that were, they show all the frequent patterns that can be mined from datasets. It is not uncommon that, for many real-life applications, the user may end up be overwhelmed by such a huge number of patterns. In this paper, we propose a visualizer—called CloseViz—to show the user only the useful patterns. Specifically, CloseViz shows only closed frequent patterns. By doing so, CloseViz reduces the number of displayed patterns to a useful amount while retaining all the important frequency information. Moreover, CloseViz presents the closed frequent patterns to the user in a useful manner, which allows visual exploration of the patterns. Note that the closed patterns shown by CloseViz can be considered as surrogates for all the frequent patterns that can be mined from the datasets.


FpVAT

May 2010

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9 Reads

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39 Citations

ACM SIGKDD Explorations Newsletter

As frequent pattern mining plays an essential role in many knowledge discovery and data mining (KDD) tasks, numerous algorithms for finding frequent patterns have been proposed over the past 15 years. However, most of these algorithms return the mining results in the form of textual lists containing frequent patterns showing those frequently occurring sets of items. It is well known that "a picture is worth a thousand words". The use of visual representation can enhance the user's understanding of the inherent relations in a collection of frequent patterns. In this paper, we develop a simple yet useful visual analytic tool for supporting frequent pattern mining called FpVAT . Such a visual analytic tool consists of two modules: One module gives users an overview so that they can derive insight from a massive amount of raw data; another module enables users to perform analytical reasoning on the mining results via interactive visual interfaces so that users can detect the expected frequent patterns and discover the unexpected frequent patterns. As a visual analytic tool, our FpVAT is equipped with several interactive features for effective visual support in the data analysis and KDD process for various real-life applications.


Figure 3: Snapshots of our proposed FpViz showing the collapsed and expanded views.  
Figure 4: Two visualizers showing the same set of frequent patterns mined from a student-course DB.  
FpViz: A visualizer for frequent pattern mining

June 2009

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530 Reads

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24 Citations

Over the past 15 years, numerous algorithms have been proposed for frequent pattern mining as it plays an essential role in many knowledge discovery and data mining (KDD) tasks. Most of these frequent pattern mining algorithms return the mined results in the form of textual lists containing frequent patterns showing those frequently occurring sets of items. It is well known that "a picture is worth a thousand words". The use of visual representation can enhance the user understanding of the inherent relations in a collection of frequent patterns. A few visualizers have been developed to visualize the input data or the mined results. However, most of these visualizers were not designed for visualizing the mined frequent patterns. In this paper, we develop a visualizer for frequent pattern mining. Such a visualizer - called FpViz - gives users an insight about the data, allows them to zoom in and zoom out, and provides details on demand. Moreover, FpViz is also equipped with several interactive features for effective visual support in the data analysis and KDD process for various real-life applications.


Citations (12)


... Using Hypertext Markup Language (HTML), Scalable Vector Graphics (SVG), and Cascading Style Sheets (CSS), D3 allows data to bring to life. Plus, D3.js is a JavaScript framework that allows web browsers to create dynamic, interactive data visualization [12]. Usually, D3.js were used to visualize data in order to help for better understanding [13]. ...

Reference:

Sentiment Analysis on Students Stress and Depression Due to Online Distance Learning During the COVID-19 Pandemic
Interactive Visual Analytics of Big Data
  • Citing Chapter
  • January 2017

... The data mining algorithms represent long textual patterns that visually incomprehensive rather than beneficial with visually informative that humans can interact with the computers. In this context, the HCI meets data mining where the HCI and its result such as visualization help (Leung et al., 2016) data miners to visualize data and analyze mined results. The HCI is immensely used in these fields for proper visualization of data into meaningful information for including general users. ...

Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns
  • Citing Conference Paper
  • September 2016

Lecture Notes in Computer Science

... • the first category is the works that use the different visualisation techniques as tool of visualisation of results of mining association rules process like cubix (Melo et al., 2012), VisAR (Techapichetvanich and Datta, 2005), FIRE (Mukherji et al., 2013), MIME (Goethals et al., 2011), FIsViz (Leung et al., 2008), and iVAS (Leung and Carmichael, 2011) • the second category is the set of works that use parallel coordinates in visual mining association rules process like Bruzzese et al. (2003) and Yang (2005) • the last category that uses the visualisation technique as tool to mining association rules like Yamamoto et al. (2008). ...

iVAS: An Interactive Visual Analytic System for Frequent Set Mining

... Fister et al. [27] have proposed a method for identifying dependencies among mined association rules based on population-based metaheuristics and complex networks. However, there are also generic tools, like the CloseViz [28] and the SPMF open-source data mining library Version 2 [29], specialized primarily in pattern mining, offering visual implementation of discovered data mined by ML algorithms that could also be used for visualization of ARM. ...

CloseViz: Visualizing useful patterns

... Authors argue on a point that: a legible right-side-up graph to one user may become an illegible upside-down graph towards another user. [13] FpViz represents each frequent pattern consisting of k items by a horizontal line connecting k nodes where each node represents an item within the frequent pattern. If multiple patterns have the same frequency, the corresponding lines representing these patterns are collapsed into one line. ...

FpViz: A visualizer for frequent pattern mining

... Besides the data mining task of classification, researchers have also examined relevant problems of detecting communities over social and information networks 6,7 . Furthermore, researchers have also examined other data mining tasks including clustering of social media data 27,30 , mining and analysis of co-authorship networks 17,20 , and visualization of social networks 9,16 . This paper, on the other hand, focuses on a different but also important aspect-namely, pattern mining on social networks. ...

Visual Analytics of Social Networks: Mining and Visualizing Co-authorship Networks
  • Citing Conference Paper
  • July 2011

Lecture Notes in Computer Science

... Frequent patterns can be presented in graphical form which allows visual exploration of frequent patters that promotes visualized business analytics. [4,5,6] proposed visualization techniques for visualizing closed patterns. [7,8,9,10,11] uses Scatter plots, Mosaic plots and Parallel co-ordinate plots to display data as a collection of points. ...

FIsViz: A Frequent Itemset Visualizer
  • Citing Conference Paper
  • May 2008

... Different levels of veracity (e.g., precise data, imprecise and uncertain data [1,25,36]) characterized these big data. Examples of big data include financial time series [8,33,45], social network data [23,26,32,37,41], transportation data [6,2,34,35,40], omic (e.g., genomic) data [3,44], as well as disease reports ...

Exploring Social Networks: A Frequent Pattern Visualization Approach
  • Citing Conference Paper
  • August 2010

... This explains why the big data are sometimes considered as the "new oil". Hence, data science solutions [34][35][36]-which aim to discover knowledge and information from big data via data mining algorithms [37][38][39], machine learning tools [40][41][42][43], mathematical and statistical models [44], informatics [45,46], and visualization [47][48][49][50][51][52]-for big data analytics and mining are in demand. For instance, analyzing and mining big biodiversity and environmental data could lead to some insights about our environments and ecosystem, and thus could help policy and decision makers take appropriate actions to further enhance the environments and ecosystem. ...

Visually Contrast Two Collections of Frequent Patterns
  • Citing Conference Paper
  • December 2011