International Journal Of Computational Engineering Research (ijceronline.com) Vol. 3 Issue. 3 198 ||Issn||2250-3005|| (Online) ||March||2013|| ||www.ijceronline.com|| Implementation of Data Mining Techniques for Weather Report Guidance for Ships Using Global Positioning System P.Hemalatha M.E Computer Science And Engineering IFET College Of Engineering Villupuram Abstract This paper deals with the implementation of data mining methods for guiding the path of the ships. The implementation uses a Global Positioning System(GPS) which helps in identifying the area in which the ship is currently navigating. The weather report on that area is compared with the existing database and the decision is made in accordance with the output obtained from the Data Mining technique. This decision about the weather condition of the navigating path is then instructed to the ship. This paper highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis. GLOBAL POSITIONING SYSTEM(GPS) provides specially coded satellite signals that can be processed in a GPS receiver enabling the receiver to compute position, velocity and time. Satellites were first used in position finding in a simple but reliable 2D Navy system called „Transit‟ which laid the ground work for a system-“The Global Positioning System” that is funded and controlled by US Dept of Defense (DOD). 1. INTRODUCTION: DATA MINING: Data Mining means decision-making and data extraction. It also generates prediction mechanism from the available history. This implementation uses the Classification Models of Data Mining techniques.Data mining is a process of inferring knowledge from such huge data. Data Mining has three major components 1. Clustering or Classification, 2. Association Rules and 3.Sequence Analysis. In classification/clustering we analyze a set of data and generate a set of grouping rules which can be used to classify future data. An association rule is a rule which implies certain association relationships among a set of objects in a database. In this process we discover a set of association rules at multiple levels of abstraction from the relevant set(s) of data in a database. In sequential Analysis, we seek to discover patterns that occur in sequence. This deals with data that appear in separate transactions (as opposed to data that appear in the same transaction in the case of association). 2. CLASSIFICATION MODEL: In Data classification one develops a description or model for each class in a database, based on the features present in a set of class-labeled training data. There have been many data classification methods studied, including decision-tree methods, such as C4.5, statistical methods, neural networks, rough sets, database-oriented methods etc. Using the training set, the Classification attempts to generate the description of the classes and these descriptions help to classify the unknown records. In addition to the training set, we can also have a test data set which is used to determine the effectiveness of a classification. The goal of the Classification is to build a concise model called Decision Tree that can be used to predict the class of the records whose class label is not known. 3. DECISION TREES: A Decision tree is a Classification scheme, which generates a tree and a set of rules, representing the model of different classes, from a given data set. The set of records available for developing Classification methods is generally divided into two distinct subsets- a training set and a test set. The former is used for deriving the classifier, while the latter is used to measure the accuracy of the Classifier. The accuracy of the classifier is determined by the percentage of the test examples that are correctly classified. This implementation