Introduction to Soft Computing for Knowledge Discovery and Data Mining (original) (raw)

Soft-Computing Based Data Mining : A Review

2015

Soft computing is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost by seeking for an approximate solution to an imprecisely/precisely formulated problem. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) are most widely applied in the data mining step of the overall KDD process. Fuzzy sets provide a natural framework for the process in dealing with uncertainty. Neural networks and rough sets are widely used for classification and rule generation. Genetic algorithms (GAs) are involved in various optimization and search p...

Knowledge Discovery in Data Mining Using Soft Computing Techniques – A Comparative Analysis

2016

Decades of research have focused on data mining is a multidisciplinary field and also popularly referred to as knowledge discovery from data. In the field of evolutionary computation in data mining provides a balance mixture of theory, algorithm and application. Genetic algorithm is search algorithm based on the mechanics of natural selection, crossover and mutation to reach the optimal solution for better performance. Genetic algorithm is easily parallelizable and has been used for different techniques like as classification, clustering and other optimization problem. Genetic algorithm using clustering is a data stream mining task which is very useful to gain insight of data and its characteristics it comes under the pre-processing step in all mining process. Clustering is a useful unsupervised data mining task that subdivides an input data set into a desired number of subgroups so that members of the same subgroup will have high similarity and the members of different groups have ...

SKAD’11–Soft Computing Applications and Knowledge Discovery

2011

Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution in real life tasks. This volume contains the papers presented at SCAKD-2011: The International Workshop on Soft Computing Applications and Knowledge Discovery held on June 24, 2011 in Moscow.

Soft computing for intelligent data analysis

analysis

Intelligent data analysis (IDA) is an interdisci- plinary study concerned with the effective analysis of data. This paper will briefly look at some of the key issues in intelligent data analysis, discuss the opportu- nities for soft computing in this context, and present .several IDA case studies ...

A Review of Soft Computing Techniques and Applications

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICRADL – 2021, 2021

Soft Computing can be defined as a science of thinking, reasoning that helps to deal with complex systems. Its main aim is to develop intelligent machines in order to solve realworld problems. It differs from the conventional hard computing as it can handle uncertainty, imprecision easily. It includes use of different techniques such as machine learning, artificial neural networks etc. that can be used together for solving complex problems that are difficult to tackle using conventional models of mathematics. These techniques play a vital role in identifying hidden patterns from the data and doing the classification for making intelligent decisions. This paper reviews some of the soft computing techniques and its applications.