Remote Sensing is a multidisciplinary technique for image acquisition and measurement of information. Remote sensing analysis paved way for satellite image classification which facilitates the image interpretation of large amount of data. Satellite Images covers large geographical span and results in the exploitation of huge information which includes classifying into different sectors. Different classification algorithms exist for image classification, but with the wide range of applications an algorithm with improved performance in terms of accuracy is required. Here in this paper we analyze different methods of supervised classification, different post classification techniques, spectral contextual classification and provide a comparative study on their efficiency.
Clustering is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and kmedoids. The different hierarchical techniques studied here are BIRCH and CHAMELEON. The different grid-based techniques which are described are DBSCAN and DENCLUE. Lastly, the different techniques which are used in grid-based technique, like STING and CLIQUE are described. This paper aims to provide a brief overview and comparison of these different clustering algorithms and methods.
Almost all the search engines that exist retrieve web pages by finding the exact keywords. The traditional keyword-based search engines suffer many problems, like synonyms and terms similar to keywords are not taken into account to search web pages, they treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another.
As crime rates keep spiralling each day, new challenges are faced by law enforcement agencies. They have to keep their forces on the lookout for any signs of criminal activity. This may only cause more burden on their resources. The law enforcement agencies should therefore be able to predict such increases or decreases or trends in crime, such as the approximate number of murders, rapes, thefts, or any such crimes that may occur in a particular area in a particular month, year, or any timespan, or, the overall number of crimes occurring in a country in a particular year in the future, or any other prediction or projection of future crime statistics. First, our system proposes to extract data from crime record repositories, on which we intend to perform data mining. Data classification and regression algorithms then help in forecasting and predicting this is proposed to be done by first training a set and then applying the learned rules on the test set in order to determine the predicted output. Using this, law enforcement agencies can better understand how the crime pattern across a certain region, or interval of time is, and using this data, such agencies can take proactive action to stem the rise of particular crimes in particular regions, or during particular times. This would save them a lot of time, money and effort. Our system proposes to mine this data and thus run appropriate algorithms on such data. This predicted output could also be presented to the user in the form of clusters using a data visualization algorithm like K-means clustering algorithm. The final endproduct could thus be a system where some future predictions would made by training crime data sets, and the output could be visualized in order to be simple to comprehend for the user.
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