講座名稱(chēng)：計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院系列報(bào)告

講座時(shí)間：6月24日15:00

講座地點(diǎn)：北校區(qū)主樓II區(qū)319

主辦單位：計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院

報(bào)告1：Subgraph Matching: Past and Present

講座人介紹：

林學(xué)民，澳大利亞新南威爾士大學(xué)杰出教授，是該校數(shù)據(jù)庫(kù)與知識(shí)研究組負(fù)責(zé)人，IEEE Fellow。它的研究興趣包括數(shù)據(jù)庫(kù)、數(shù)據(jù)挖掘、算法設(shè)計(jì)等，特別是在大規(guī)模復(fù)雜非結(jié)構(gòu)化數(shù)據(jù)（如圖、時(shí)空、流、文本及不確定數(shù)據(jù)）的可擴(kuò)展處理與挖掘上。

講座內(nèi)容：

Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will focus on a fundamental problem, subgraph matching. I will cover solutions for single computer, as well as distributed solutions.

報(bào)告2：An Introduction of Model-based Text Clustering

講座人介紹：

尹建華博士目前是山東大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院副教授。他2012年在西電獲得學(xué)士學(xué)位，2017年于清華大學(xué)獲得博士學(xué)位。他曾訪(fǎng)問(wèn)UIUC。它的主要研究方向包括文本聚類(lèi)和貝葉斯推斷。

講座內(nèi)容：

Text clustering is an important technology in data mining and machine learning. It is widely used in event discovery and tracking, document summarization, search results clustering, and other issues. Although there are many researches on text clustering, there are still many challenging problems to be solved: (1) How to set the number of clusters? Is it possible to automatically discover the number of clusters from the data? (2) How to deal with the sparsity of short text? (3) How to automatically discover abnormal documents in a dataset? (4) How to deal with the concept drift problem of stream text clustering? In this report, Dr. Jianhua Yin will share his work on text clustering and the stories behind these papers when he was a PhD student at Tsinghua University, hoping to inspire the younger students who are interested in scientific research.