Informatics Publishing Limited

Dhaval Patel ¹, Amit Ganatra ², C. K. Bhensdadia ¹

Affiliations
1 Dharamsinh Desai University, Nadiad, District Kheda, Gujarat, IN
2 CHARUSAT, Changa, District Anand, Gujarat, IN

Abstract

The quantity of accessible information on the web continues to grow rapidly and has exceeded human processing capabilities. The sheer amount of the information increases the complexity for users from discovering desired information. Recommendation systems have become a valuable resource for users seeking intelligent ways to search through enormous volume of information available to them. Web logs are important information repository which records users activates on search results. The mining of these logs can improve the performance of search engines, since user has a specific goal when searching for information. In this paper, a survey is provided on the different recommendation techniques with their advantage and drawbacks. A brief comparison of different personalisation techniques based on certain parameters is done.

Keywords

Log Mining, Personalisation, Recommendation Techniques, Web Usage Mining.

Full Text

     

Amit Thakkar ¹, Reshma Idresh Lakhani ², Amit Ganatra ²

Affiliations
1 Chandubhai S Patel Institute of Technology Changa, Gujarat, IN
2 Chandubhai S Patel Institute of Technology, Changa, Gujarat, IN

Abstract

Data Mining can be considered an analytic process designed to explore business or market data to search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. Data mining is useful for prediction. We can improve accuracy of different classifiers by combining various classifiers and taking their predictions. One such method is Stacking, an ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. This enhances the benefits obtained by individual classifiers. This paper is a review work of different approaches proposed by various authors in their paper.

Keywords

Ensemble of Classifiers, Bagging, Boosting, Staking, Troika.

Full Text

     

Amit Thakkar ¹, Nikita Bhatt ¹, Amit Ganatra ¹, Arpita Shah ¹

Affiliations
1 Charotar Institute of Technology Changa, Nadiad, Gujarat, IN

Abstract

Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroid. K-means clustering algorithm often does not work well for high dimension; hence, to improve the efficiency, we apply PCA, dimensionality reduction technique, on data set and obtain a reduced dataset containing possibly uncorrelated variables. The challenging task for any clustering method is to determine the number of clusters beforehand. To find the number of cluster, we apply EM method that finds number of clusters user should choose by determining a mixture of Gaussians that fit a given data set. Finally the experiment results shows that the use of techniques such as PCA and EM, improve the efficiency of K-means clustering.

Keywords

Cluster, EM, K-Mean, PCA.

Full Text

     

Nidhi Shah ¹, Amit Ganatra ¹, C. K. Bhensdadia ², Y. P. Kosta ¹

Affiliations
1 Department of Computer Engineering CIT-Changa, Gujarat, IN
2 Department of Computer Engineering, Dharmsinh Desai University Nadiad, Gujarat, IN

Abstract

Given the explosive rate of data deposition on the web; classification has become a complex and dynamic phenomenon. As classification complexity is continuing to grow, so is the need in direct proportion to designing and developing data mining algorithms & techniques. Classification is the most commonly applied data mining technique, a process of finding a set of models or functions that describes and distinguishes data classes, for the purpose of using it – so classification is a specialist with specialized skills, which is moving toward universality. A classification problem is considered as a supervised learning problem. The aim of the classification task is to discover a kind of relationship between the attributes (input) and class (output), so that the discovered knowledge can be used to predict the class of a new unknown object. Classification of the records or data is done based on the classification rules. Ant colony optimization is a method that derives its inspiration from real ants that forage for food by selecting the shortest path from multiple possible paths available to reach food. Thus merging the concept of Ant Colony Optimization (ACO) with data mining brings in a new approach to designing classification rule that will be helpful in extraction of information for a specialized dataset. In this paper a survey is done on Ant-miner algorithm for classification Rule extraction. The Ant miner algorithm extract classification rule from data using if-then-else pattern; similar to other traditional algorithm available for classification task or purposes. Extraction of classification Rule from data is an important task of data mining. We present, detailed description about the algorithm available for classification rule mining using Ant colony optimization. Variations to the ant colony based an Ant-miner algorithm is discussed along with the comparison of the algorithms with critical parameters like predictive accuracy, No. of Rules Discovered, No. of terms per No. of rules Discovered, using different data sets. Hence the paper will help to study various ant miner algorithms and comparison carried out will help the data miner to select and use algorithm according to need based on the specialized properties associated with the algorithm.

Keywords

Ant Colony Optimization (ACO), Classification, Data Mining.

Full Text

     

Kamal Sutaria ¹, Amit Ganatra ², Y. P. Kosta ¹, C. K. Bhensdadia ³, Kruti Khalpada ¹

Affiliations
1 Charotar University of Science and Technology, Changa, Gujrat, IN
2 Charotar University of Science and Technology Changa, Gujrat, IN
3 Department of Computer Engineering, Dharamsinh Desai University, Nadiad, Gujarat, IN

Abstract

Incremental Flexible Frequency Discretization (IFFD) is a recently proposed discretization approach for Naïve Bayes (NB).IFFD performs satisfactory by setting the minimal interval frequency for discretized intervals as a fixed number. In this paper, we first argue that this setting cannot guarantee that the selecting MinBinSize is on always optimal for all the different datasets. So the performance of Naïve Bayes is not good in terms of classification error. We thus proposed a sequential search method for NB: named Optimum Binning. Experiments were conducted on 4 datasets from UCI machine learning repository and performance was compared between NB trained on the data discretized by OB, IFFD, and PKID.

Keywords

Discretization, Naïve Bayes, Optimum Binning.

Full Text

     

Amit Thakkar ¹, Yogeshwar P. Kosta ², Amit Ganatra ²

Affiliations
1 Charotar Institute of Technology Changa, Gujarat, IN
2 Charotar Institute of Technology, Changa, Gujarat, IN

Abstract

Classification is a process of sorting unknown values of certain attributes-of-interest based on the values of other attributes, and is a major challenge in data mining. A commonly used method is the decision tree. The efficiency of decision tree algorithms has been well established for relatively small data sets. However, this method of classification has problems when handling larger data sets, data having continuous numerical values, and has the tendency to favor multiplicity in terms of values associated with the attributes in the data set while making selection of the final determining attribute. In data mining applications, large training sets are common; therefore decision tree algorithms have limitations of scalability. Also in most data mining application, users have a little knowledge regarding which signature attribute should be selected for effective mining and the user is more dependent upon the capability of the algorithm. In this paper, we address selection of two things, one, the right signature attribute and the second, handle large data set. This we accomplish by proposing a new data classification method through integration of a set of sequential process that involves steps such as data cleaning; attribute oriented induction (identifying the signature attribute), relevance analysis as the preprocessing steps followed by induction of decision trees. This stepwise approach helps us to set simple extraction rules at multiple levels of abstraction and easily handles large data sets and continuous numerical values in a scalable way.

Keywords

Data Mining, Classification, Data Cleaning, Decision Tree Induction, Relevance Analysis.

Full Text

     

Purvi Rekh ¹, Amit Thakkar ², Amit Ganatra ³

Affiliations
1 Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN
2 Department of Information and Technology, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN
3 U & PU Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN

Abstract

With the development of Internet and the emergence of a large number of text resources, the automatic text classification has become a research hotspot. As number of training documents increases, accuracy of Text Classification increases. Traditional classifiers (Supervised learning) use only labeled data for training. Labeled instances are often difficult, expensive, or time consuming to obtain. Meanwhile unlabeled data may be relatively easy to collect. Semi-Supervised Learning makes use of both labeled and unlabeled data. Several researchers have given algorithms for Text Classification using Semi-Supervised Learning. But still improving accuracy of Text Classification using Semi-Supervised Learning is a challenge. In the iterative process in the standard Expectation Maximization (EM) based semi-supervised learning, some unlabeled samples are misclassified by the current classifier because the initial labeled samples are not enough. To overcome this limitation, an EM based Semi-Supervised Learning algorithm using Naïve Bayesian and Support vector machine is proposed in this paper to improve accuracy of text classification using semi-supervised learning.

Keywords

Expectation Maximization (EM), Naïve Bayes (NB), Support Vector Machine (SVM), Semi-Supervised Machine (SSL).

Full Text

     

Amit Ganatra ¹, Y. P. Kosta ¹

Affiliations
1 Patel Department of Computer Engineering, Charotar University of Science & Technology, CHARUSAT, Gujarat, IN

Abstract

Stock prediction is, so far, one of the popular topics not only for research purposes but also for commercial applications. Owing to its importance, a well-established school of concepts and techniques, including fundamental and technical analysis, has developed in recent decades. However, because these techniques or tools are based on totally different analytical approaches, they often yield contradictory results. More importantly, these analytical tools are heavily dependent on human expertise and justification in areas such as the location of reversal (or continuation) patterns, market patterns, and trend prediction. Predicting stock data with traditional time series analysis has proven to be difficult. An artificial neural network may be more suitable for the task primarily because no assumption about a suitable mathematical model has to be made prior to forecasting. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Our focus of study is to build neural network for stock market prediction. We propose to study feed forward back propagation network and their predictive accuracy. We propose to study architecture model of neural network and its different network parameters. The study attempts to understand network parameter like momentum, learning rate, number of neurons etc. We will compare architecture and result of above models. Our aim is to build best model by studying various parameters of the neural network. And also study other related model to compare accuracy of the model. In this study we have used R tool to implement the neural network. We have taken closing price, turnover, global indices, interest rate, and inflation as a neural network input. We proposed to include other indicator like news, currency rate, and crude price as input to the neural network. We compared stock prediction accuracy by setting different network parameters. Subsequently, an attempt is made to build and evaluate a neural network with different network parameters.  Technical as well as fundamental data are used as input to the network. In benchmark comparisons, the price prediction proves to be successful.

Keywords

Classification, Neural Network, Feature Selection, Prediction, Stock Market.

Full Text

     

Parth Shah ¹, Amit Ganatra ²

Affiliations
1 Department of Information Technology, Chandubhai S Patel Institute of Technology, Charusat, Changa - 388421, Gujarat, IN
2 Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Charusat, Changa - 388421, Gujarat, IN

Abstract

Background/Objectives: Remote data storage becomes the hype nowadays as various organizations provide free access of the application. Security and efficiency are the major concerns while using such kind of application. Hence solution related to data integrity verification needs to be focused to achieve reliability. Methods/Statistical Analysis: Despite of all the hype surrounding the storage and security solutions, clients are still hesitant to deploy their business in the cloud. As security is the major concerns which may become the hindrance as clients are more concern about data privacy and data protection. In proposed work we have provided solution of efficiency and security related to remote data storage using additive homomorphic encryption. Findings: Solution to this can be provided using some of the data integrity proofs techniques. The advent of an advanced model of the security to provide the solution should not negotiate with the required efficiency and reliability present in the current solutions. Additive homomorphic encryption has been proven to efficient compared to multiplicative homomorphic encryption. Additive homomorphic encryption is efficient compare to multiplicative homomorphic encryption which provides better efficiency. Applications/Improvements: Methodology given could be useful for remote file storage applications. Proposed algorithm has been implemented and tested. The results shows that it gives improvement over the existing solution in terms of tag generation and verification.

Keywords

Additive Homomorphism, Data Integrity Proof, Encryption, Efficiency, Remote Storage.

Full Text

   

Amit Ganatra ¹, Y. P. Kosta ¹, Gaurang Panchal ¹, Chintan Gajjar ²

Affiliations
1 Department of Computer Engineering, Charotar Institute of Technology, Charotar University of Science and Technology, Changa, Anand-388 421, IN
2 Information Technology Department, Charotar Institute of Technology, Charotar University of Science and Technology, Changa, Anand-388 421, IN

Abstract

An Artificial Neural Network classifier is a nonparametric classifier. It does not need any priori knowledge regarding the statistical distribution of the class in a giver selected data Source. While, neural network can be trained to distinguish the criteria used to classify easily in a generalized manner that allows successful classification the newly arrived inputs not used during training. Through this paper it is eastliblished that back propagation neural network works successfully for the purpose of classification. Back propagation suffers from getting stuck into Local Minima. Weight optimization in Back propagation can be optimized using the Genetic Algorithm (GA). The back propagation algorithm is improved by invoking Genetic algorithm, to improve the overall performance of the classifier. The performance of a fitness algorithm using the approach suggested by us is a Hybrid System that is being analyzed in this paper. In this paper the issue of improving the fitness (weight adjustment) of Back propagation algorithm is addressed. Some of the Advantages of Hybrid algorithms are: convergence speed will be increased and the local minima problem can be overcome. The proposed Hybrid Algorithm is to perform learning as a back propagation and optimize weights using GA for classification.

Keywords

Artificial Neural Network, Back Propagation Algorithm, Genetic Algorithm.

Full Text