As web is expanding day by day and people generally rely on web for communication so e-mails are the fastest way to send information from one place to another. Now a day’s all the transactions all the communication whether general or of business taking place through e-mails. E-mail is an effective tool for communication as it saves a lot of time and cost. But e-mails are also affected by attacks which include Spam Mails. Spam is the use of electronic messaging systems to send bulk data. Spam is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. In this study, we analyze various data mining approach to spam dataset in order to find out the best classifier for email classification. In this paper we analyze the performance of various classifiers with feature selection algorithm and without feature selection algorithm. Initially we experiment with the entire dataset without selecting the features and apply classifiers one by one and check the results. Then we apply Best-First feature selection algorithm in order to select the desired features and then apply various classifiers for classification. In this study it has been found that results are improved in terms of accuracy when we embed feature selection process in the experiment. Finally we found Random Tree as best classifier for spam mail classification with accuracy = 99.72%. Still none of the algorithm achieves 100% accuracy in classifying spam emails but Random Tree is very nearby to that.

Software repositories contain wealth of information about software code, designs, execution history, code and design changes, bug database, software release and software evolution. To meet increased pressure of releasing updated or new versions of software systems due to changing requirements of stakeholder, software are rarely built from scratch. Software reusability is a primary attribute of software quality which aims to create new software systems with a likelihood of using existing software components to add, modify or delete functionalities in order to adapt to new requirements imposed by stakeholders. Software reuse using software components or modules provide a vehicle for planning and re-using already built software components efficiently. In this paper, we propose a framework for our approach to predict software reusable components from existing software repository on the basis of (1) stakeholders intention (requirement) match and (2) similarity index count for better reuse prediction. To effectively manage storage and retrieval of relevant information we use concept of situational method engineering to match and analyze the information for reuse. We use Genetic algorithm, Rabin Karp algorithm for feature selection and classification and k-means clustering methods of data mining to refine our results of prediction in order to better manage and produce high quality software systems within estimated time and cost.