In our study, we proposed a novel Neuro-fuzzy classification technique. The inputs to the Neuro-fuzzy classification system were fuzzified by applying the Gaussian curve membership function. The proposed method considered a fuzzification matrix in which the input features were associated with a degree of membership to different classes. Based on the value of degree of membership a feature would be attributed to a specific category or class. We applied our method to five benchmark datasets from the UCI machine language repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with Multilayer Perceptron Backpropagation Network (MLPBPN) algorithm in terms of different performance measures like Accuracy, Root-mean-square error, Kappa statistic, True Positive Rate, False Positive Rate, Precision, Recall, and F-Measure. In every aspect the proposed method performed better than MLPBPN.
ATA mining has attracted many researchers and analysts in the information industry and in research organizations as a whole in the last decades, due to the availability of large amounts of data and the impending need for changing such data into meaningful information and knowledge. The useful information and knowledge gained can be used for applications ranging from market survey, customer retention, and production control to evolutionary analysis and science exploration [1], [2].
Classification as an important data mining technique involves extracting interesting patterns representing knowledge from large real-world databases. Such analysis can provide a deep insight into the better understanding of different large-scale databases. The study related to effective knowledge development is ...
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The advent of neural net with the seminal work of Hopfield , popularized the use of machine intelligence techniques in the pattern recognition. However, the dense and inherent structure of neural networks is not suitable for VLSI implementation. So, researchers in the neural network domain tried to simplify the structure of the neural network by pruning unnecessary connections. Simultaneously, the CA research community explored the advantages of the sparse network structure of cellular automata for relevant applications. The hybridization of cellularity and neural network has given rise to the popular concept of cellular neural networks.
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Key Words; Artificial Intelligence, Multiple Intelligence, Fuzzy Logic, Fuzzy Logic Toolbox, Vocational Guidance, Decision Making
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Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
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- Data mining finds hidden pattern in data sets and association between the patterns. To achieve the objective of data mining association rule mining is one of the important techniques. This paper presents a survey on three different association rule mining algorithms FP Growth, Apriori and Eclat algorithm and their drawbacks which would be helpful to find new solution for the problems found in these algorithms The comparison of algorithms based on the aspects like different support value.
The field of neural networks involves a new approach to computing that uses mathematical structures with the ability to learn (Zsolutions). These methods were inspired by investigations into modeling nervous system learning (Zsolutions). For example, neurons in the human brain are used to transmit data back and forth to each other. Artificial neural networks use this same technique to process various kinds of information (Fu, p 4).
Singh, Y. & Chauhan, A. (2009). Neural networks in data mining. Journal of Theoretical and
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Today, artificial intelligence techniques have found application area in many areas. The most often techniques which are investigated and used, are:
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.