Advances in Information Mining

44-51

Authors: RAJKUMAR, SAKHARE D.Y

In recent times, the necessity of generating single document summary has gained popularity among the researchers due to its extensive applicability. Most of the automatic text summarization systems utilize extraction-based techniques for selecting the most significant portions of text to generate coherent summaries. In this paper we will analyze the performance of fuzzified neural network approach with the graph theory approach. In the proposed system, we have developed an efficient automatic text summarization system based neural network and fuzzy logic. In the training phase at first, the feature vector is computed for a set of sentences using the feature extraction technique. After that, the feature vector and their corresponding fuzzy score are used to train the neural network optimally. Later in the testing phase, the input document is subjected to preprocessing and feature extraction techniques. In order to obtain the sentence score for every sentence in the input document, the feature vector is fed to the trained neural network that returns the sentence score for every sentence. Finally, by making use of sentence score, the most important sentences are extracted from the input document. The experimentation is performed with the DUC 2002 dataset and the generated summary is evaluated with the measures such as Precision, recall and f-measure. The comparative results of our proposed approach with the graph theory approach produces better results by means of different compression rates.

57-66

Authors: SAIGAL S., MEHROTRA D

This paper focuses on the methodology used in applying the Time Series Data Mining techniques to financial time series data for calculating currency exchange rates of US dollars to Indian Rupees. Four Models namely Multiple Regression in Excel, Multiple Linear Regression of Dedicated Time Series Analysis in Weka, Vector Autoregressive Model in R and Neural Network Model using NeuralWorks Predict are analyzed. All the models are compared on the basis of the forecasting errors generated by them. Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) are used as a forecast accuracy measure. Results show that all the models accurately predict the exchange rates, but Multiple Linear Regression of Dedicated Time Series Analysis in Weka outperforms the other three models.

19-25

Authors: CRISTIAN BISCONTI, ANGELO CORALLO, HOSSAM FARIS, SALVATORE TOTARO

Latent Semantic Analysis (LSA) is a powerful statistical theory and method for indexing, retrieving and analyzing textual information. With the increased demand for more efficient databases and computing power, a lot of research and work has focused on the practical side of the LSA. In this paper we introduce a design and implementation of an SQL extension for LSA applications. The developed SQL extension for LSA is aimed to utilized as an easy and simple interface for basic LSA and cosine similarity functions provided for indexing, searching and retrieving large textual information stored in open source relational database management system like PostgresSQL.

33-43

Authors: TAHEREH SADAT MOUSAVI, BARAANI DASTJERDI

In CSCW systems, the end results are subject on how personnel participate and work specifically in a flexible dynamic environment. Thus, it has become imperative to develop a comprehensive system whereby the analysts can understand and analyze the workflow in order to enhance personnel support, cooperation processes and the final results. In other to achieve this goal, a system whereby individual and groups send feedbacks to the personnel are advocated. If analysts have access to the workflow and personnel information during the execution of work, they will be able to achieve the above-mentioned goals faster through analysis and sending feedback, especially in operational environments in which time is important. In this article, an Incremental Process-mining technique is presented to increase efficiency in a multi perspective process model during the execution of work. This technique is integrated within the ProM6 framework. The results obtained from the proposed algorithm which is based on criteria such as Fitness, Cost-Based Fitness etc tends to agree with alpha algorithm.

1-4

Authors: Reddy P.V.N., Satya Prasad K

In this paper a retrieval study for natural image learning environment is proposed. The content of this paper is a result of projects called Image content-based retrieval on a natural image database. The objective of this project is to develop an image content-based search engine, which can perform identity check of a natural image. It is well known that conventional natural image databases can only be retrieved by text-based query. In this paper we use the shape, color, and other features extracted from a captured natural image to search the natural image database. The developed technique is able to perform scale, translation, and rotation invariant matching between natural images. Currently, the database contains several hundreds of natural images. In future, we shall enhance the capability of the search engine to deal with more than 30,000 natural image species, which is the total amount of natural image species along the coast.

11-13

Authors: Ajmire P.E., Warkhede S.E

The work in this paper is deals with the recognition of handwritten Marathi vowels. Marathi is an Indo-Aryan language spoken by about 71 million people mainly in the Indian state of Maharashtra and neighbouring states. Marathi is also spoken in Israel and Mauritius. Marathi is thought to be a descendent of Maharashtri, one of the Prakrit languages which developed from Sanskrit. This work is based on invariant moments for recognition of isolated Marathi Handwritten Characters and their divisions. The proposed technique is independent of size, slant, orientation, translation and other variations in handwritten characters. Handwritten Marathi Characters/Numerals are more complex for recognition than corresponding English characters due to many possible variations in order, number, direction and shape of the constituent strokes. The work treats isolated Characters as an image of 40X40 pixel size. Seven invariant central moments of the image and two additional feature sets are evaluated. 10 samples of each number from 20 different writers have been sampled and prepared a database has been made. Seven central invariant moments are evaluated for each character and its parts by dividing it by two different ways. In all, there are 14 features corresponding to each character. The Gaussian Distribution Function has been adopted for classification. The average success rate of some vowels is compatible.

1-7

Authors: Lohgaonkar M.H., Bajaj V.H., Jadhav V.A., Patwari M.B

The aim of this paper is to present a fuzzy multi-objective multi-index transportation problem and develop multi-objective multi-index fuzzy programming model. This model cannot only satisfy more of the actual requirements of the integral system but is also more flexible than conventional transportation problems. Furthermore, it can offer more information to the decision maker (DM) for reference, and then it can raise the quality for decision-making. This paper, we use a special type of linear and non-linear membership functions to solve the multi-objective multi-index transportation problem. It gives an optimal compromise solution.

13-17

Authors: Sankar K., Krishnamoorthy K

Ant-based techniques, in the computer sciences, are designed those who take biological inspirations on the behavior of these social insects. Data clustering techniques are classification algorithms that have a wide range of applications, from Biology to Image processing and Data presentation. Since real life ants do perform clustering and sorting of objects among their many activities, we expect that an study of ant colonies can provide new insights for clustering techniques. The aim of clustering is to separate a set of data points into self-similar groups such that the points that belong to the same group are more similar than the points belonging to different groups. Each group is called a cluster. Data may be clustered using an iterative version of the Fuzzy C means (FCM) algorithm, but the draw back of FCM algorithm is that it is very sensitive to cluster center initialization because the search is based on the hill climbing heuristic. The ant based algorithm provides a relevant partition of data without any knowledge of the initial cluster centers. In the past researchers have used ant based algorithms which are based on stochastic principles coupled with the k-means algorithm. The proposed system in this work use the Fuzzy C means algorithm as the deterministic algorithm for ant optimization. The proposed model is used after reformulation and the partitions obtained from the ant based algorithm were better optimized than those from randomly initialized hard C Means. The proposed technique executes the ant fuzzy in parallel for multiple clusters. This would enhance the speed and accuracy of cluster formation for the required system problem.

1-4

Authors: Anandhavalli M., Suraj Kumar Sudhanshu, Ayush Kumar , Ghose M.K

In general the rule generated by association rule mining algorithms like priori, partition, pincer-search, incremental, border algorithm etc, does not consider negation occurrence of the attribute in them and also these rules have only one attribute in the consequent part. By using Genetic Algorithm (GAs) the system can predict the rules which contain negative attributes in the generated rules along with more than one attribute in consequent part. The major advantage of using GAs in the discovery of prediction rules is that they perform global search and its 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 possible optimized rules from given data set using genetic algorithm.

11-13

Authors: Chitra Desai , Manisha Patil , Sahebrao Shinde

The ability to handle complex data has made object oriented database gain a wide attention in the field of scientific research and engineering. Using object-oriented database for scientific data in it has several characteristics that should be considered for storing and retrieving it for future use. As this information needs to be exchanged and shared, XML in itself is emerging as a standard for information exchange over heterogeneous systems. The object oriented database structure designed using UML standard can be transformed into XML DTD. The XML DTD can further be used for obtaining DTD graph, which is further, used for extracting object oriented database schema by applying set of rules for class definition. This paper takes an example XML schema and transforms it into relational database schema. As object oriented database can be looked for more complex type of data handling compared to relational database, the XML schema is transformed to XML DTD and is used for representing object oriented database schema.