[Colloq] Hiring Talk **Wednesday, March 6, 10:30am**

[Rachel Bates]

College of Computer and Information Science Colloquium

presents
Aris Gionis

  who will speak on:
Proximity Problems for Non-Euclidean Data

Wednesday, March 6, 2003
10:30am
149 Cullinane Hall
Northeastern University



ABSTRACT
Efficient algorithms for proximity problems have been among the most
commonly used tools for data mining and data analysis. Two such problems are
similarity search and clustering. Similarity search is necessary in order to
locate relevant information in a database. Clustering, a form of data
summarization can facilitate visualization, understanding, and knowledge
extraction.
Proximity problems are typically studied for data represented as points in
the Euclidean space. In this talk, I will investigate instances of proximity
problems for data that are more complex than Euclidean points. First, I will
describe a hashing scheme for efficient indexing of data represented as
sets. I will show how this scheme can be applied to provide a scalable
solution to the problem of retrieving web pages similar to a query page.
Also, I will present a technique for automatic evaluation of web page
representation strategies.
In the second part of the talk, I will address the problem of clustering
temporal data. I will motivate the problem with an application in the area
of genomic data analysis, and I will discuss the connection of temporal
clustering with the problem of segmenting sequences and with traditional
clustering.

Host:  Betty Salzberg