Sciweavers

TSP
2008
167views more  TSP 2008»
13 years 10 months ago
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data
A new hierarchical nonparametric Bayesian framework is proposed for the problem of multi-task learning (MTL) with sequential data. The models for multiple tasks, each characterize...
Kai Ni, John William Paisley, Lawrence Carin, Davi...
IDA
2008
Springer
13 years 11 months ago
Schema matching on streams with accuracy guarantees
Abstract. We address the problem of matching imperfectly documented schemas of data streams and large databases. Instancelevel schema matching algorithms identify likely correspond...
Szymon Jaroszewicz, Lenka Ivantysynova, Tobias Sch...
FPL
2008
Springer
122views Hardware» more  FPL 2008»
14 years 1 months ago
Mining Association Rules with systolic trees
Association Rules Mining (ARM) algorithms are designed to find sets of frequently occurring items in large databases. ARM applications have found their way into a variety of field...
Song Sun, Joseph Zambreno
VLDB
1995
ACM
163views Database» more  VLDB 1995»
14 years 3 months ago
An Efficient Algorithm for Mining Association Rules in Large Databases
Mining for a.ssociation rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an effic...
Ashok Savasere, Edward Omiecinski, Shamkant B. Nav...
KDD
1995
ACM
129views Data Mining» more  KDD 1995»
14 years 3 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
PODS
1998
ACM
134views Database» more  PODS 1998»
14 years 3 months ago
A New Framework For Itemset Generation
The problem of finding association rules in a large database of sales transactions has been widely studied in the literature, We discuss some of the weaknessesof the large itemset...
Charu C. Aggarwal, Philip S. Yu
SIGMETRICS
2000
ACM
14 years 3 months ago
An analytical model of the working-set sizes in decision-support systems
This paper presents an analytical model to study how working sets scale with database size and other applications parameters in decision-support systems (DSS). The model uses appl...
Magnus Karlsson, Per Stenström
DEXAW
2000
IEEE
112views Database» more  DEXAW 2000»
14 years 3 months ago
An Experiment in Discovering Association Rules in the Legal Domain
In this paper we explore the applicability of an algorithm designed for finding association rules in large databases to the discovery of relevant associations from a large case ba...
Trevor J. M. Bench-Capon, Frans Coenen, Paul H. Le...
SBACPAD
2003
IEEE
180views Hardware» more  SBACPAD 2003»
14 years 4 months ago
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
ICDM
2003
IEEE
154views Data Mining» more  ICDM 2003»
14 years 4 months ago
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
Pattern-based clustering is important in many applications, such as DNA micro-array data analysis, automatic recommendation systems and target marketing systems. However, pattern-...
Jian Pei, Xiaoling Zhang, Moonjung Cho, Haixun Wan...