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KDD
1995
ACM
129views Data Mining» more  KDD 1995»
15 years 1 months ago
Feature Extraction for Massive Data Mining
Techniques for learning from data typically require data to be in standard form. Measurements must be encoded in a numerical format such as binary true-or-false features, numerica...
V. Seshadri, Raguram Sasisekharan, Sholom M. Weiss
SIGMOD
1993
ACM
134views Database» more  SIGMOD 1993»
15 years 2 months ago
Mining Association Rules between Sets of Items in Large Databases
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an e cient algorithm that generates all s...
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami
DAGSTUHL
2007
14 years 11 months ago
Subspace outlier mining in large multimedia databases
Abstract. Increasingly large multimedia databases in life sciences, ecommerce, or monitoring applications cannot be browsed manually, but require automatic knowledge discovery in d...
Ira Assent, Ralph Krieger, Emmanuel Müller, T...
95
Voted
ICDE
2006
IEEE
192views Database» more  ICDE 2006»
15 years 4 months ago
Mining Executive Compensation Data from SEC Filings
In recent years, corporate governance has become a more and more important concern in investment decision-making. As one of the most important factors in evaluating corporate gove...
Chengmin Ding, Ping Chen
ER
2001
Springer
136views Database» more  ER 2001»
15 years 2 months ago
A Randomized Approach for the Incremental Design of an Evolving Data Warehouse
A Data Warehouse (DW) can be used to integrate data from multiple distributed data sources. A DW can be seen as a set of materialized views that determine its schema and its conten...
Dimitri Theodoratos, Theodore Dalamagas, Alkis Sim...