280,288 research outputs found
Set-oriented data mining in relational databases
Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing.\ud
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In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases
RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS
In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions
A Survey of Parallel Data Mining
With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons
learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms
On the Suitability of Genetic-Based Algorithms for Data Mining
Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search strategies are of vital importance. Search strategies on genetic-based algorithms have been applied successfully in a wide range of applications. We focus on the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
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