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

Procedia Computer Science

et al. 2014

DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper. The algorithm makes use of a specialized data structure, called DiSE-tree, for effectively and efficiently representing relevant information on diverse social entities while successfully supporting the mining phase. Diverse entities are popular in a wide spectrum of application scenarios, ranging from linked Web data to Semantic Web and social networks. In all these application scenarios, it has become important to analyze high volumes of valuable linked data and discover those diverse social entities. We complement our analytical contributions by means of an experimental evaluation that clearly shows the benefits of our tree-based diverse social entity mining algorithm.

Section: Related Workmentioning

confidence: 99%

A Tree-based Algorithm for Mining Diverse Social Entities

Braun

Cuzzocrea

Procedia Computer Science

et al. 2014

“…x As "a picture is worth a thousand words", it is desirable to represent the discovered knowledge via visual analytics [60] for achieving high visibility.…”

Section: Introductionmentioning

confidence: 99%

Big Data Science on COVID-19 Data

2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE)

Chen

Shang

et al. 2020

In the current era of big data, high volume of big data can be generated and collected from a wide variety of rich data sources at a rapid rate. Embedded in these big data are useful information and valuable knowledge. Examples include healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a data science solution for analyzing big COVID-19 epidemiological data. The solution helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our data science solution in discovering useful knowledge from big COVID-19 data.

“…Since its introduction [2], the research problem of frequent patterns mining has been the subject of numerous studies. Some studies extended the notion of frequent patterns to other interesting patterns (e.g., correlated patterns [17], emerging patterns [15], frequent sub-graphs [32], popular patterns [26], strong groups of friends in social networks [10]); some other studies focused on efficient mining and visualization of patterns [13,21,23,25].…”

Section: Introductionmentioning

confidence: 99%

A constrained frequent pattern mining system for handling aggregate constraints

Proceedings of the 16th International Database Engineering &Amp; Applications Sysmposium on - IDEAS '12

Jiang

Sun

et al. 2012

Self Cite

Frequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions of the entire collection of frequent patterns. To mine patterns that satisfy the user aggregate constraints in the form of agg(X.attr)θconst, properties of constraints are exploited. When agg is sum, the mining can be complicated. Existing mining systems or algorithms usually make assumptions about the value or range of X.attr and/or const. In this paper, we propose a frequent pattern mining system that avoids making these assumptions and that effectively handles the sum constraints as well as other aggregate constraints.