CSE012/CS059 Data Mining
Winter 2017
|
|
Material
Books and Slides
·
Material
from the book Introduction to
Data Mining by Tan, Steinbach, Kumar. ·
Mining Massive Datasets by
Anand Rajaraman, Jeff Ullman, and Jure Leskovec. Free
online book. Includes
slides from
the course. ·
Introduction to Information Retrieval
by C. Manning, P. Raghavan, H. Schutze. Free online
book. ·
Networks Crowds
and Markets by D. Easley, J. Kleinberg. Free
online book. ·
Social
Media Mining by R. Zafarani, M. Ali Abbasi, H. Liu. Free online book. ·
Material
from the book Data Mining: Concepts and
Techniques, by Jiawei Han and Micheline Kamber. Python
·
Notes
from the course Computational
Tools for Data Science in BU ·
Cookbooks: Includes examples of the use of
Iron Python, code, and data. Useful Unix Commands
You may
find the following unix commands useful when pre-processing data: ·
cut: allows you to get specific
columns from delimited data ·
sort: sorts the rows of a file in
lexicographic order, n for numeric ·
uniq: merges consecutive rows of a file
that are identical. ·
grep: finds a sting within a file Do man
<command> in unix/linux shell to get more information. Software
·
WEKA Data Mining Software: A
software package that implements multiple data mining tools. ·
FIMI: Frequent Itemsets Mining Implementation:
A repository of implementations for frequent itemset mining. All
implementations assume the input format of the example datasets: text file
where each row is a basket consisting of space separated integers that
represent the items. ·
Liblinear: Software
package for classification. Implements the Logistic Regression and SVM classifiers. Datasets
·
The Yelp Academic Challenge
dataset ·
UCI Machine Learning Repository o Τhe Iris dataset (ARFF file).Τhe link to UCI
repository. o The
SpamBase dataset (ARFF
file). Τhe
link to UCI
repository o The
Mushroom dataset (ARFF
file). The link
to UCI repository. ·
Movie Lens Datasets by GroupLens Research
·
FourSquare tips with categories: a collection
of FourSquare tips on restaurants in New York (thanks to Yiannis Kotrotsios).
·
FourSquare tips with categories: a collection
of FourSquare tips with the category of the corresponding venue for
restaurants, nightlife venues, and shops in New York (thanks to Yiannis
Kotrotsios).
·
FourSquare users and venues: a collection
of pairs of user ids and venue names in New York, where the user with the
specific id has left a tip to the venue with the specific name on Foursquare
(thanks to Yiannis Kotrotsios).
·
Twitter
data from the paper What
is Twitter, a Social Network, or a News Media? by Haewoon Kwak,
Changhyun Lee, Hosung Park, and Sue Moon. For the first Assignment, you need
the Restricted
User Profiles data file. The fields in the file are explained on the
page, you are interested in the eleventh field which is the profile description.
·
English Stopwords.
Txt file with a list of English stopwords.
·
SpamAssassin.
·
Stanford Network Analysis Project Datasets.
·
Movie-Actor Graph. Each line in the file is a tab-separated movie-actor pair, i.e., it
corresponds to one edge in the graph.
|