Christopher L. Carmichael

Efficient Mining of Frequent Patterns from Uncertain Data

Since its introduction, mining of frequent patterns has been the subject of numerous studies. Generally, they focus on improving algorithmic efficiency for finding frequent patterns or on extending the notion of frequent patterns to other interesting patterns. Most of these studies find patterns from traditional transaction databases, in which the content of each transaction-namely, items-is definitely known and precise. However, there are many real-life situations in which ones are uncertain about the content of transactions. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability. Experimental results show the efficiency of our algorithm over its non-tree-based counterpart.

WiFIsViz: Effective Visualization of Frequent Itemsets

Frequent itemset mining plays an essential role in the mining of many different patterns. Most existing frequent itemset mining algorithms return the mined results--namely, frequent itemsets--in the form of textual lists. However, the use of visual representation can enhance the user understanding of the inherent relations in a collection of frequent itemsets. In this paper, we propose an effective visualizer, called WiFIsViz, to display the mined frequent itemsets. WiFIsViz provides users with an overview and details about the itemsets. Moreover, this visualizer is also equipped with several interactive features for effective visualization of the frequent itemsets mined from various real-life applications.

Exploring Social Networks: A Frequent Pattern Visualization Approach

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.

Visual analytics of social networks: mining and visualizing co-authorship networks

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.

Visually Contrast Two Collections of Frequent Patterns

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.

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

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.