Publications Search

@inproceedings{neville2000iterative,

title = {Iterative classification in relational data},

author = {Jennifer Neville and David Jensen},

url = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.5425&rep=rep1&type=pdf},

year  = {2000},

date = {2000-01-01},

booktitle = {Proc. AAAI-2000 workshop on learning statistical models from relational data},

pages = {13--20},

abstract = {Relational data offer a unique opportunity for improving the classification accuracy of statistical models. If two objects are related, inferring something about one object can aid inferences about the other. We present an iterative classification procedure that exploits this characteristic of relational data. This approach uses simple Bayesian classifiers in an iterative fashion, dynamically updating the attributes of some objects as inferences are made about related objects. Inferences made with high confidence in initial iterations are fed back into the data and are used to inform subsequent inferences about related objects. We evaluate the performance of this approach on a binary classification task. Experiments indicate that iterative classification significantly increases accuracy when compared to a single-pass approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/gd/Jensen00,

title = {Knowledge Discovery from Graphs (Invited Talk)},

author = {David Jensen},

url = {https://doi.org/10.1007/3-540-44541-2_16},

doi = {10.1007/3-540-44541-2_16},

year  = {2000},

date = {2000-01-01},

booktitle = {Graph Drawing, 8th International Symposium, GD 2000, Colonial Williamsburg, 

 VA, USA, September 20-23, 2000, Proceedings},

volume = {1984},

pages = {170},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {Knowledge discovery is the process of discovering useful and previously unknown knowledge by analyzing large databases. Knowledge discovery is also sometimes called “data mining” or “applied machine learning.” A new generation of knowledge discovery tools are beginning to address data that can be expressed as large graphs. Example applications include fraud detection in telecommunication networks and classifying Web pages based on hyperlink structure. These new technologies for knowledge discovery are becoming increasingly relevant to graph drawing. Specifically, graph drawing can aid the process of knowledge discovery by providing visualizations that reveal useful patterns in the data. Conversely, knowledge discovery can provide guidance for graph drawing by identifying recurring substructures or by classifying nodes into distinct types. Attempts to exploit the synergy between the two fields raises interesting new research questions. How should knowledge about a domain affect the drawing of graphs about that domain? What types of knowledge are most easily discovered using visualization, as opposed to automated statistical algorithms? These questions were posed in the context of several examples of knowledge discovery applied to large graphical data sets.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}