Data mining is the process of extracting hidden patterns from the given data. With the explosive increase of data every year, data mining is becoming an increasingly important tool to transform this data in to information. In this exploration, the authors attempt to apply various data clustering techniques to a home interview survey data related to transportation planning. Real world databases contain a lot of noisy, missing and inconsistent data because of huge size and often errors occurred during data collection. In order to improve the quality of data mining results, they need to preprocess the data by applying various techniques.