In the spread of information, how to quickly find one's favorite movie in a large number of movies become a very important issue. Personalized recommendation system can play an important role especially when the user has no clear target movie. In this paper, we design and implement a movie recommendation system prototype combined with the actual needs of movie recommendation through researching of KNN algorithm and collaborative filtering algorithm. Then we give a detailed principle and architecture of JAVAEE system relational database model. Finally, the test results showed that the system has a good recommendation effect.

Along with the rapid development of electronic products, there is a large amount of electronic waste, such as hundreds of millions of mobile phones and junk personal computers, which is not only a waste, but also a tremendous damage to the environment. The article presents a technique of reusing the electronic waste for the smart home monitoring system. It also describes how to set up the prototype of the system. Compared with the present normal household monitoring system, the technique will save lots of expenditure. And it can effectively reduce the cost in protecting the environment. Especially it can promote the smart home into ordinary homes better as well as faster.

No abstract

Clustering is an important technique in machine learning, which has been successfully applied in many applications such as text and webpage classifications, but less in transaction database classification. A large organization usually has many branches and accumulates a huge amount of data in their branch databases called multidatabases. At present, the best way of mining multidatabases is, first, to classify them into different classes. In this paper, we redefine related concepts of transaction database clustering, and then in connection to the traditional clustering method, we propose a strategy of clustering transaction databases based on the k-mean. To prove that our strategy is effective and efficient, we implement the proposed algorithms. The results showed that the method of clustering transaction databases based on the k-mean is better than present methods.