loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Lixin Fu

Affiliation: University of North Carolina at Greensboro, United States

Keyword(s): Classification, Decision Trees, Data Cube

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Strategic Decision Support Systems

Abstract: Data classification is an important problem in data mining. The traditional classification algorithms based on decision trees have been widely used due to their fast model construction and good model understandability. However, the existing decision tree algorithms need to recursively partition dataset into subsets according to some splitting criteria i.e. they still have to repeatedly compute the records belonging to a node (called F-sets) and then compute the splits for the node. For large data sets, this requires multiple passes of original dataset and therefore is often infeasible in many applications. In this paper we present a new approach to constructing decision trees using pre-computed data cube. We use statistics trees to compute the data cube and then build a decision tree on top of it. Mining on aggregated data stored in data cube will be much more efficient than directly mining on flat data files or relational databases. Since data cube server is usually a required compo nent in an analytical system for answering OLAP queries, we essentially provide “free” classification by eliminating the dominant I/O overhead of scanning the massive original data set. Our new algorithm generates trees of the same prediction accuracy as existing decision tree algorithms such as SPRINT and RainForest but improves performance significantly. In this paper we also give a system architecture that integrates DBMS, OLAP, and data mining seamlessly. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.147.205.154

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Fu, L. (2005). CONSTRUCTION OF DECISION TREES USING DATA CUBE. In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-19-8; ISSN 2184-4992, SciTePress, pages 119-126. DOI: 10.5220/0002509801190126

@conference{iceis05,
author={Lixin Fu.},
title={CONSTRUCTION OF DECISION TREES USING DATA CUBE},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2005},
pages={119-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002509801190126},
isbn={972-8865-19-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - CONSTRUCTION OF DECISION TREES USING DATA CUBE
SN - 972-8865-19-8
IS - 2184-4992
AU - Fu, L.
PY - 2005
SP - 119
EP - 126
DO - 10.5220/0002509801190126
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic