Mining Frequent Patterns from Multi-Dimensional Relational Sequences (original) (raw)

Multi-dimensional relational sequence mining

Fundamenta Informaticae, 2008

Abstract. The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes ...

Multi-Relational Data Mining A Comprehensive Survey

2015

Multi-Relational Data Mining or MRDM is a growing research area focuses on discovering hidden patterns and useful knowledge from relational databases. While the vast majority of data mining algorithms and techniques look for patterns in a flat single-table data representation, the sub-domain of MRDM looks for patterns that involve multiple tables (relations) from a relational database. This subdomain has received an increased research attention during the last two decades due to the wide range of possible applications. As a result of that growing attention, many successful multi-relational data mining algorithms and techniques were presented. This chapter presents a comprehensive review about multi-relational data mining. It discusses the different approaches researchers have followed to explore the relational search space while highlighting some of the most significant challenges facing researchers working in this sub-domain. The chapter also describes number of MRDM systems that h...

Eliciting Multi-Dimensional Relational Patterns

Here the issue of discovery of frequent multi-dimensional patterns from relational sequences is addressed. The great variety of applications of sequential pattern mining makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multi-dimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Relational Learning algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences.

MR-Radix: a multi-relational data mining algorithm

Human-centric Computing and Information Sciences, 2012

Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix tha...

A Review: Data mining over Multi-Relations

International Journal of Computer Applications, 2013

In this paper, Multi-relational data mining enables pattern mining from multiple tables. Multi-relational data mining algorithms can be used as practical proposal to overcome the deficiency of conventional algorithms. Multi-relational data mining algorithms directly extract frequent patterns from different registers in efficient manner without need of transfer the data in a single table will, on the other hand, used the available memory space is not enough to ensure the production of large amounts of data. For this reason, and the use of space, algorithms are an integral care for the prospection of large repositories. The paper provides the overview of multi relation data mining techniques and classification algorithms. It also defines the frequent pattern mining. The presented paper discussed the various architecture and issues related to multi table data mining. A lot of literature has been proposed in this area. Some of them has discussed in this paper.

A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

Journal of Computer Science, 2018

Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multirelational data mining algorithm based on association rules, called TBMR-Radix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not.

MRAR: Mining Multi-Relation Association Rules

Journal of Computing and Security

In this paper, we introduce a new class of association rules (ARs) named "Multi-Relation Association Rules" which in contrast to primitive ARs (that are usually extracted from multi-relational databases), each rule item consists of one entity and several relations. These relations indicate indirect relationship between entities. Consider the following Multi-Relation Association Rule where the first item consists of three relations live in, nearby and humid: "Those who live in a place which is near by a city with humid climate type and also are younger than 20 → their health condition is good". A new algorithm called MRAR is proposed to extract such rules from directed graphs with labeled edges which are constructed from RDBMSs or semantic web data. Also, the question "how to convert RDBMS data or semantic web data to a directed graph with labeled edges?" is answered. In order to evaluate the proposed algorithm, some experiments are performed on a sample dataset and also a real-world drug semantic web dataset. Obtained results confirm the ability of the proposed algorithm in mining Multi-Relation Association Rules.

Mining Frequent Patterns on Object-Relational Data

Data mining is viewed as an essential part of the process towards knowledge discovery. Through data mining process different kinds of patterns that is frequent pattern and others, are discovered, evaluated and presented as knowledge. Mining data from large database repositories which contains vast amount of data is not only interesting but also essential since it yields useful and novel information which aid organizations and other individuals in decision making. Many previous research works based on frequent pattern mining algorithm and association rule mining are focused on transactional data, yet there are other interesting types of data which requires just about same study in order to perform mining techniques on them so as to discover novel information. This paper elaborates mining of Object-relational data in relation to transactional data as a base of understanding, later uses differed mining algorithms to uncover frequent patterns and evaluate performances of these algorithms. Two approaches for this mining task was proposed, namely fundamental approach and nested-relations approach.

Multi-relational Algorithm for Mining Association Rules in Large Databases

2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2011

Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MR-Radix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth.

On Multi-Relational Data Mining for Foundation of Data Mining

2007 IEEE/ACS International Conference on Computer Systems and Applications, 2007

Multi-Relational Data Mining (MRDM) deals with knowledge discovery from relational databases consisting of one or multiple tables. As a typical technique for MRDM, inductive logic programming (ILP) has the power of dealing with reasoning related to various data mining tasks in a "unified" way. Like granular computing (GrC), ILP-based MRDM models the data and the mining process on these data through intension and extension of concepts. Unlike GrC, however, the inference ability of ILP-based MRDM lies in the powerful Prolog-like search engine. Although this important feature suggests that through ILP, MRDM can contribute to the foundation of data mining (FDM), the interesting perspective of "ILPbased MRDMfor FDM" has not been investigated in the past. In this paper, we examine this perspective. We provide justification and observations, and report results of related experiments. The primary objective of this paper is to draw attention to FDM researchers from the ILP-based MRDMperspective.