Research And Implementation On Data Mining Technology Of Community Smart Service Oriented

In recent years, electronic intelligence community service has been a breakthroughin China which could combine modern information technology with the traditionalcommunity service effectively. However, data mining techniques that is applied to showthe depth of data expediently and intuitively could promote community service toachieve intelligent.As one of the important contents in data mining, clustering mainly focused on therecords from the database to find data similarity and categorize these data, so that it candiscover useful information hidden in the database. The traditional clustering algorithmhas also been a great deal of attention in data mining applications. Genetic algorithmcombined with clustering can avoid clustering into a local optimum so that couldachieve better clustering results.First, this paper introduced the basic concepts and analytical methods of datamining, focused on the cluster techniques and the basic theory of genetic algorithms.For the problem that k-means algorithm can’t get the best number of clusters and is sosensitive to its initialization that could be easy to fall into local optimization, this paperproposed an improved algorithm which is based on genetic algorithms. Algorithm forclustering uses genetic algorithms which combine floating-point code withvariable-length chromosome coding. Crossover in genetic algorithm change the numberof the cluster center set,so that can search as much as possible the center set programin the search process, Which has a global search ability.Finally, based on the previous research, we introduced a genetic optimizationclustering algorithm to the Community Intelligent Service System, which achieved goodresults.