Earticle

Android is an Operating System that is more powerful supporting a large number of smartphones. Android applications are developed using Java, and so it can be easily ported to the new platform. The primary aim of introducing PHP Android application is that the extension of data storage from SQLite to the MySQL. Here the PHP is used to fetch the data from the MySQL database at the web server. The PHP page will use JSON parsing for reading the data from the database. Here there will be communication between PHP and Android application. The PHP page will contact MySQL database and will fetch the data and returns the result. Our e-municipal is designed to automate the activities of Municipal Corporation that deal with different day to day general public amenities. The primary aim of this application is to ease the user to communicate with Municipal Corporation via a handheld Android application. The general user can easily register themselves and quickly send a petition to the municipal corporation regarding the problems they face in Metro water connection, sanitation and electricity maintenance. They can quickly record the birth and death and also can check their complaint status. The user can also view the tender which is applied by the municipality and can apply for it. Corporation officers and employees can see all the complaints from different users on different problems. Only the admin have the power to change all the data from the user. Here the tender allocation was based on spatial data mining algorithm rather than traditional clustering algorithm.

We extended the online learning strategy and scalable clustering technique to soft subspace clustering, and propose two online soft subspace clustering methods, OFWSC and OEWSC. The proposed evolving soft subspace clustering algorithms can not only reveal the important local subspace characteristics of high dimensional data, but also leverage on the effectiveness of online learning scheme, as well as the ability of scalable clustering methods for the large or streaming data. Furthermore, we apply our proposed algorithms to text clustering of information retrieval, gene expression data clustering, face image classification and the problem of predicting disulfide connectivity.

공간자료의 예측문제에 있어 전통적 예측방법인 크리깅 방법과 최근 통계적 문제에 적용되기 시작한 신경망 분석방법 간의 비교를 사례연구를 통해 행하였다. 일반적으로 크리깅에 의한 선형예측은 공간자료에 대한 일반적 통계모형으로서 간주되어 왔다. 한편 예측문제에 있어 신경망모형에 기초한 비모수적 방법이 관심의 대상이 되고 있으며 특히 대용량 자료의 경우 데이터마이닝 기법의 한 분야로 널리 사용되고 있는 실정이다. 본 연구에서는 공간 자료의 예측에 있어 유전자 알고리즘과 신경망 분석 모형을 결합하여 기존의 크리깅 방법과의 예측력을 비교한다.주요용어: 공간자료, 크리깅, 유전자 알고리즘, 신경망분석

In spatial statistics, the kriging procedure gives an optimal linear predictor of a spatial process at a point x_0`