Learning Different Concept Hierarchies and the Relations Between them from Classified Data

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

Download Full-text

The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

Download Full-text

OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Download Full-text

Data Mining System and Construction Analysis of Skilled Talents based on Big Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/3/032021 ◽

2021 ◽

Vol 1744 (3) ◽

pp. 032021

Author(s):

Yibo Guan

Keyword(s):

Data Mining ◽

Big Data ◽

Mining System ◽

Data Mining System

Download Full-text

An intelligent data mining system for drop test analysis of electronic products

IEEE Transactions on Electronics Packaging Manufacturing ◽

10.1109/6104.956808 ◽

2001 ◽

Vol 24 (3) ◽

pp. 222-231 ◽

Cited By ~ 16

Author(s):

Chi Zhou ◽

P.C. Nelson ◽

Weimin Xiao ◽

T.M. Tirpak ◽

S.A. Lane

Keyword(s):

Data Mining ◽

Drop Test ◽

Mining System ◽

Test Analysis ◽

Electronic Products ◽

Data Mining System

Download Full-text

Criminal Network Analysis inside Law Enforcement Agencies: A Data-Mining System Approach under the National Intelligence Model

International Journal of Police Science & Management ◽

10.1350/ijps.2013.15.4.321 ◽

2013 ◽

Vol 15 (4) ◽

pp. 323-337 ◽

Cited By ~ 7

Author(s):

Patrick Seidler ◽

Rick Adderley

Keyword(s):

Data Mining ◽

Law Enforcement ◽

Network Analysis ◽

System Approach ◽

Law Enforcement Agencies ◽

Mining System ◽

Criminal Network ◽

Data Mining System ◽

Criminal Network Analysis

Download Full-text

A framework for a World Wide Web-based Data Mining system

Journal of Network and Computer Applications ◽

10.1006/jnca.1998.0071 ◽

1998 ◽

Vol 21 (3) ◽

pp. 163-185 ◽

Cited By ~ 2

Author(s):

Johnny S.K. Wong ◽

Rishi Nayar ◽

Armin R. Mikler

Keyword(s):

Data Mining ◽

World Wide Web ◽

World Wide ◽

Mining System ◽

Web Based ◽

Data Mining System

Download Full-text

Dimension Reduction for Objects Composed of Vector Sets

International Journal of Applied Mathematics and Computer Science ◽

10.1515/amcs-2017-0012 ◽

2017 ◽

Vol 27 (1) ◽

pp. 169-180 ◽

Cited By ~ 1

Author(s):

Marton Szemenyei ◽

Ferenc Vajda

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Discriminant Analysis ◽

Probability Distribution ◽

Dimension Reduction ◽

Pose Estimation ◽

Real World ◽

Single Object ◽

Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

Download Full-text