Soumadip Ghosh

Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm.

Weather Data Mining using Artificial Neural Network

Weather Data Mining is a form of Data mining concerned with finding hidden patterns inside largely available meteorological data, so that the information retrieved can be transformed into usable knowledge. A variety of data mining tools and techniques are available in the industry, but they have been used in a very limited way for meteorological data. In this paper, a neural network-based algorithm for predicting the atmosphere for a future time and a given location is presented. We have used Back Propagation Neural (BPN) Network for initial modelling. The results obtained by BPN model are fed to a Hopfield Network. The performance of our proposed ANN-based method (BPN and Hopfield Network based combined approach) tested on 3 years weather data set comprising 15000 records containing attributes like temperature, humidity and wind speed. The prediction error is found to be very less and the learning converges very sharply. The main focus of this paper is based on predictive data mining by which we can extract interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of meteorological data.

A comparative study of breast cancer detection based on SVM and MLP BPN classifier

The breast cancer is a severe disease found among females all over the world. This is a type of cancer disease arising from human breast tissue cells, usually from the lobules or the inner lining of the milk ducts that provide the ducts with milk. A recent medical survey reveals that throughout the world breast cancer occurs in 22.9% of all cancers in women and it also causes 13.7% of cancer deaths in them. Breast cancer, being very harmful to all women, may cause loss of breasts or may even cost their life. Diagnosis of breast cancer disease is an important area of data mining research. In our work, different classification techniques are applied on the benchmark Breast Cancer Wisconsin dataset from the UCI machine language repository for detection of breast cancer. Principal component analysis (PCA) technique has been used to reduce the dimension of the dataset. Our objectives is to diagnose and analyze breast cancer disease with the help of two well-known classifiers, namely, MLP using Backpropagation NN (MLP BPN) and Support Vector Machine (SVM) and, thereafter assess their performance in terms of different performance measures like Accuracy, Precision, Recall, F-Measure, Kappa statistic etc.

A Tutorial on Different Classification Techniques for Remotely Sensed Imagery Datasets

Classification techniques are used on large databases to develop models describing different data classes. Such analysis can provide deep insight for better understanding of different large-scale databases. Studies related to knowledge acquisition and effective knowledge development are also very popular in the remote sensing field with satellite imagery datasets. In any remote sensing research, the decision-making process mainly depends on the effectiveness of the classification process. Efficient classification techniques were developed and applied to the Statlog (Landsat Satellite) database at the University of California, Irvine Machine Learning Repository to identify six land type classes. We used three different classification algorithms on the large satellite imagery: multilayer perceptron backpropagation neural network (MLP BPNN), support vector machine (SVM), and k-nearest neighbor (k-NN). This research study aimed to evaluate the performance of these classification algorithms in the prediction of the classified lands from this large set of satellite imagery. We used different performance measures, such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and F-measure to evaluate the performance of each classifier. Among the three classification techniques applied, MLP BPNN turned out to be the best; next was k-NN, followed by SVM.

Association rule mining algorithms and Genetic Algorithm: A comparative study

Generally frequent itemsets are extracted from large databases by applying association rule mining (ARM) algorithms like Apriori, Partition, Pincer-Search, Incremental, and Border algorithm etc. Genetic Algorithm (GA) can also be applied to discover the frequent patterns from databases. The main advantage of using GA in the discovery of frequent patterns or itemsets is that they can perform global search and its time complexity is lesser compared to other Apriori-based algorithms as because it is based on the greedy approach. But the FP-tree algorithm is considered to be the best among the ARM algorithms, because its candidate sets generation procedure is completely different from Apriori-based algorithms. The major aim of this paper is to present a comparative study among ARM-based and GA-based approaches to data mining.

Soil Classification From Large Imagery Databases Using a Neuro-Fuzzy Classifier

In this paper, we propose a neuro-fuzzy (NF) classification technique to determine various soil classes from large imagery soil databases. The technique looks at the feature-wise degree of belongings of the imagery databases to obtainable soil classes using a fuzzification method. The fuzzification method builds a membership matrix with an element count equal to the mathematical product of the number of data records and soil classes present. The elements of this matrix are the input to a neural network model. We apply our technique to three UCI databases, namely, Statlog Landsat Satellite, Forest Covertype, and Wilt for soil classification. The paper aims to find out soil classes using the proposed technique, and then compare its performance with four well-known classification algorithms, namely, radial basis function network, k-nearest neighbor, support vector machine, and adaptive NF inference system. Numerous measures, for example, root-mean-square error, kappa statistic, accuracy, false positive rate, true positive rate, precision, recall, F-measure, and area under the curve, are used for evaluating the quantitative analysis of the simulated results. All these evaluation measures approve the supremacy of the proposed NF method.

A Comparative Study to the Bank Market Prediction

Bank market prediction is an important area of data mining research. In the present scenario, we are given with huge amounts of data from different banking organizations, but we are yet to achieve meaningful information from them. Data mining procedures will help us extracting interesting knowledge from this dataset to help in bank marketing campaigns. This work introduces analysis and applications of the most important techniques in data mining. In our work, we use Multilayer Perception Neural Network (MLPNN), Decision Tree (DT) and Support Vector Machine (SVM). The objective is to examine the performance of MLPNN, DT and SVM techniques on a real-world data of bank deposit subscription. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The performance is evaluated by some well-known statistical measures such as accuracy, Root-mean-square error, Kappa statistic, TP-Rate, FP-Rate, Precision, Recall, F-Measure and ROC Area values.