| dc.identifier.uri | http://hdl.handle.net/11401/77323 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | The rapid development of information technology produces vast amounts of data with numerous attributes. These multi-dimensional datasets offer tremendous opportunities for studying existing behavioral patterns and for predicting future developments. However, the high-dimensional space exceeds human comprehension. More sophisticated visualization techniques than the arsenal of standard plots are needed. First, we introduce an interactive navigation technique to help the analysts explore within the multi-dimensional data spaces. We employ a network-based interface and pair it with a parallel coordinates plot. In the network interface, the dimensions form nodes that are connected by edges representing the strength of association between dimensions. The analysts can interactively manipulate a route in the network, which is captured by the parallel coordinates plot in the form of the dimension ordering. Then, we extend the navigation interface to interactive correlation and causation analysis for both numerical and categorical variables within a unified framework. We also build a landscape (map) out of the network, which shows the raw data within the network and helps analysts quickly learn relationships and trends of the data. We demonstrate it via several applications, such as helping statisticians with model discovery. Furthermore, we prove the viability of our framework in the context of real scientific problem--climate research, and show how it helps a team of scientists make important discoveries. Finally, we introduce an interactive visual analytics interface designed for the healthcare informatics. It uses the Five-W's to establish a comprehensive multi-faceted assessment of the patient's history. The patient's multivariate data is visualized by associating each such W with a dedicated visual encoding that can represent and communicate it in effective ways. | |
| dcterms.available | 2017-09-20T16:52:31Z | |
| dcterms.contributor | Ramakrishnan, IV | en_US |
| dcterms.contributor | Mueller, Klaus | en_US |
| dcterms.contributor | Ortiz, Luis | en_US |
| dcterms.contributor | McDonnell, Kevin. | en_US |
| dcterms.creator | Zhang, Zhiyuan | |
| dcterms.dateAccepted | 2017-09-20T16:52:31Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:31Z | |
| dcterms.description | Department of Computer Science. | en_US |
| dcterms.extent | 112 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/77323 | |
| dcterms.issued | 2014-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:31Z (GMT). No. of bitstreams: 1
Zhang_grad.sunysb_0771E_11814.pdf: 34599892 bytes, checksum: 758b95d22b1d9924959140caa7fd9828 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Association Mining, Correlation Analysis, Healthcare, Information Visualization, Multivariate Data, Visual Analytics | |
| dcterms.subject | Computer science | |
| dcterms.title | VISUAL ASSOCIATION MINING OF MULTIVARIATE DATA | |
| dcterms.type | Dissertation | |
