The Application of Data Mining Techniques in Agricultural Science

Information Technology has a positive impact on other disciplines. Using today's technology, precision agriculture and InformationTechnology are mixed together. Use of Information Technology in agriculture will lead to improvements in productivity. For this purpose,the raw data is transformed into useful information through data mining. This research determined whether data mining techniques can alsobe used to improve pattern recognition and analysis of large growth factors of ornamental plants experimental datasets. Furthermore, theresearch aimed to establish data mining techniques can be used to assist in the classification and regression methods by determining whethermeaningful patterns exist various growth factors of ornamental plants characterized at various research sites across Kish Island. Differentdata mining techniques were used analyze a large data base of ornamental plants properties attributes. The data base has been collected fromdifferent plants of Kish Island in various areas in order to determine, classify and predict effective growth factors on blooming. In thisresearch, analyzed data with regression technique showed the effect of chlorophyll content on the number of flowers. The analysis of theseagricultural data base with different data mining methods may have some advantages in agricultur

Integrated Classifier: A Tool for Microarray Analysis

Microarray technology has been developed and applied in different biological context, especially for the purpose of monitoring the expression levels of thousands of genes simultaneously. In this regard, analysis of such data requires sophisticated computational tools. Hence, we confined ourselves to propose a tool for the analysis of microarray data. For this purpose, a feature selection scheme is integrated with the classical supervised classifiers like Support Vector Machine, K-Nearest Neighbor, Decision Tree and Naive Bayes, separately to improve the classification performance, named as Integrated Classifiers. Here feature selection scheme generates bootstrap samples that are used to create diverse and informative features using Principal Component Analysis. Thereafter, such features are multiplied with the original data in order create training and testing data for the classifiers. Final classification results are obtained on test data by computing posterior probability. The performance of the proposed integrated classifiers with respect to their conventional classifiers is demonstrated on 12 microarray datasets. The results show that the integrated classifiers boost the performance up to 25.90% for a dataset, while the average performance gain is 9.74%, over the conventional classifiers. The superiority of the results has also been established through statistical significance test

Data mining in soft computing framework: a survey

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

Evolutionary Modular Design of Rough Knowledge-based Network using Fuzzy Attributes

This article describes a way of integrating rough set theory with a fuzzy MLP using a modular evolutionary algorithm, for classification and rule generation in soft computing paradigm. The novelty of the method lies in applying rough set theory for extracting dependency rules directly from real-valued attribute table consisting of fuzzy membership values. This helps in preserving all the class representative points in the dependency rules by adaptively applying a threshold that automatically takes care of the shape of membership functions. An l-class classification problem is split into l two-class problems. Crude subnetwork modules are initially encoded from the dependency rules. These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilises the knowledge of the modular structure already generated, for faster convergence. The GA tunes the fuzzification parameters, and network weight and structure simultaneously, by optimising a single fitness function. This methodology helps in imposing a structure on the weights, which results in a network more suitable for rule generation. Performance of the algorithm is compared with related techniques. Keywords: Soft computing, Fuzzy MLP, Rough sets, Knowledge-based network, Genetic algorithms, Modular neural network.