Semantic Data Mining

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

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Semantic Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch269 ◽

2011 ◽

pp. 1765-1770

Author(s):

Protima Banerjee

Keyword(s):

Data Mining ◽

Web Mining ◽

Data Representation ◽

Structured Data ◽

Data Sets ◽

Semantic Data ◽

Novel Approach ◽

Symbolic Sequences ◽

Semantic Data Mining ◽

Meaningful Relationships

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from On-Line Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics.” (Washio and Motoda, 2003) The goal of any data mining endeavor is to detect and extract patterns in the data sets being examined. Semantic data mining is a novel approach that makes use of graph topology, one of the most fundamental and generic mathematical constructs, and semantic meaning, to scan semi-structured data for patterns. This technique has the potential to be especially powerful as graph data representation can capture so many types of semantic relationships. Current research efforts in this field are focused on utilizing graph-structured semantic information to derive complex and meaningful relationships in a wide variety of application areas- - national security and web mining being foremost among these. In this article, we review significant segments of recent data mining research that feed into semantic data mining and describe some promising application areas.

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Present State-of-The-Art of Association Rule Mining Algorithms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2202.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6398-6405

Keyword(s):

Data Mining ◽

Association Rule ◽

Association Rule Mining ◽

State Of The Art ◽

Synthetic Data ◽

Data Sets ◽

Evolutionary Analysis ◽

Rule Mining ◽

Transaction Database ◽

Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

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Semantic Data Mining of Financial News Articles

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40897-7_20 ◽

2013 ◽

pp. 294-307 ◽

Cited By ~ 8

Author(s):

Anže Vavpetič ◽

Petra Kralj Novak ◽

Miha Grčar ◽

Igor Mozetič ◽

Nada Lavrač

Keyword(s):

Data Mining ◽

Financial News ◽

Semantic Data ◽

Semantic Data Mining

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The Integral of Spatial Data Mining in the Era of Big Data

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch006 ◽

2017 ◽

pp. 90-126

Author(s):

Gebeyehu Belay Gebremeskel ◽

Chai Yi ◽

Zhongshi He

Keyword(s):

Data Mining ◽

Data Warehouse ◽

Spatial Data ◽

High Volume ◽

Spatial Data Mining ◽

Research Field ◽

Data Sets ◽

Data Types ◽

Basic Principles ◽

Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

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Cloud for Distributed Data Analysis Based on the Actor Model

Scientific Programming ◽

10.1155/2016/1050293 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ivan Kholod ◽

Ilya Petukhov ◽

Andrey Shorov

Keyword(s):

Data Mining ◽

Data Analysis ◽

Data Sets ◽

Distributed Data ◽

Actor Model ◽

Confidential Information ◽

Data Mining Algorithms ◽

Functional Blocks ◽

Mining Algorithms ◽

Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

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Introduction to Fuzzy Data Mining Methods

Handbook of Research on Fuzzy Information Processing in Databases ◽

10.4018/978-1-59904-853-6.ch003 ◽

2011 ◽

pp. 55-95 ◽

Cited By ~ 12

Author(s):

Balazs Feil ◽

Janos Abonyi

Keyword(s):

Data Mining ◽

Model Performance ◽

Fuzzy Rule ◽

Classification Problem ◽

Data Sets ◽

Rule Based ◽

Data Mining Algorithms ◽

Rule Bases ◽

Mining Algorithms ◽

Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

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Pattern Based Feature Construction in Semantic Data Mining

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2014010102 ◽

2014 ◽

Vol 10 (1) ◽

pp. 27-65 ◽

Cited By ~ 11

Author(s):

Agnieszka Ławrynowicz ◽

Jędrzej Potoniec

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Semantic Features ◽

Semantic Data ◽

Data Mining Approach ◽

Meta Learning ◽

New Type ◽

Domain Ontologies ◽

Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

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Using Ontologies in Semantic Data Mining with SEGS and g-SEGS

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24477-3_15 ◽

2011 ◽

pp. 165-178 ◽

Cited By ~ 15

Author(s):

Nada Lavrač ◽

Anže Vavpetič ◽

Larisa Soldatova ◽

Igor Trajkovski ◽

Petra Kralj Novak

Keyword(s):

Data Mining ◽

Semantic Data ◽

Semantic Data Mining

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A Data Warehouse Cleansing Approach Based on Mathematical Association Rules

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.1878 ◽

2012 ◽

Vol 490-495 ◽

pp. 1878-1882

Author(s):

Yu Xiang Song

Keyword(s):

Data Mining ◽

Data Warehouse ◽

Association Rules ◽

Data Sets ◽

Mathematical Association ◽

Manual Intervention ◽

Mining Association Rules ◽

High Degree

The alliance rules stated above based on the principle of data mining association rules provide a solution for detecting errors in the data sets. The errors are detected automatically. The manual intervention in the proposed algorithm is highly negligible resulting in high degree of automation and accuracy. The duplicity in the names field of the data warehouse has been remarkably cleansed and worked out. Domain independency has been achieved using the concept of integer domain which even adds on to the memory saving capability of the algorithm.

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