Data mining in soft computing framework: a survey

Abstract

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Proposing a customer knowledge management model for customer value augmentation: A home appliances case study

“…42 Sequence discovery : Sequence discovery is the identifi cation of associations or patterns over time like time series analysis. 42,68 As stated by Mitra et al , 68 ' The primary goal in sequence discovery is to model the states of the process generating the sequences or to extract and report deviation and trends over time ' . Common usages of time series analysis are in stock price forecasting.…”

“…42,44 In classifi cation task, each record has a discrete and predefi ned class label, and classifi cation models are aimed at predicting these classes for each record as accurately as possible. 42,67,68 The most common classifi cation techniques in CRM areas are neural networks, decision trees and regression. 42 Classifi cation techniques are mostly utilized in loyalty programmes, one-to-one marketing and direct marketing.…”

Functional Link Artificial Neural Network for Classification Task in Data Mining

“…The term KDD refers to the overall process of knowledge discovery in databases. Data mining is a particular step in this process, involving the application of specific algorithms for extracting patterns (models) from data [10] . Supervised pattern classification is one of the important tasks of data mining.…”

An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network

“…The main reason for combining different techniques in hybrid systems is that a single technique is often not appropriate for every domain, dataset, or stage of a system's lifecycle (Banerjee, Mitra & Pal, 1998; Mitra, Pal & Mitra, 2002). Another reason is that a hybrid system has an advantage over an approach using a single method because the technologies complement each other's shortcomings (Lingras & Butz, 2010; Indira & Ramesh, 2011).…”