データ工学特論I

データ工学特論I_E
Instructor(s)	Hiroyuki Kitagawa, Toshiyuki Amagasa, Hiroaki Shiokawa
E-Mail	H. Kitagawa: kitagawa (AT) cs.tsukuba.ac.jp, T. Amagasa: amagasa (AT) cs.tsukuba.ac.jp, H. Shiokawa: shiokawa (AT) cs.tsukuba.ac.jp
URL
Office hours	All: contact by email.
Cource#	01CH304
Area	Software System
Basic/Advanced	専門科目
Course style	Lecture (partially includes practice)
Term	FallAB
Period	Tuesday 3,4
Room#	SB0110
Keywords	Database, Data Mining, Advanced Data Management
Prerequisites	Students are encouraged to have fundamental knowledge about databases and information retrieval.
relation degree program competence	Knowledge Utilization Skills, Management Skills, Communication Skills, Research Skills, Expert Knowledge
Goal
Outline	This lecture covers basic data engineering techniques and some advanced topics. First, we briefly review database technologies which are the basis of this lecture. Then we pick up major data mining techniques and advanced topics on graph data management. The lecture is given in English.
Course plan	Basic database technologies We review basic database technologies including relational databases and object databases. Data warehouse and OLAP We talk about information integration, data warehouse, OLAP, and other releted concepts. Basic concept of data mining We explain background, purpose, and major techniques of data mining. Association Rules We explain association rules, apriori algorithm, and FP-Growth algorithm. Clustering We learn the basic concept of clustering, followed by basic algorithms, such as K-Means, hierarchical clustering, density-based clustering. We also learn the basic approach for evaluating clustering results. Graph Data Management We learn advanced topics in graph data management.
Textbook	Original texts will be distributed in the lecture.
References	P. N. Tan, M. Steinbach, A. Karpatne and V. Kumar, Introduction to Data Mining, Pearson
Evaluation	Evaluated based on final exam and quizzes.
TF / TA	TF・TA: Hiroyoshi Ito
Misc.	Lecture is given in English.