No abstract

No abstract

This paper is extended version from previous paper which proposed AOI-HEP as novel data mining technique. This paper will explain how AOI-HEP mining technique can be used to mine frequent pattern. AOI-HEP is influenced by Attribute Oriented Induction (AOI) and Emerging Pattern (EP) mining techniques by applying AOI characteristic rule algorithm and improvement EP growth rate. The experiment used adult dataset from UCI machine learning repository with 48842 instances, run in 3 seconds and the instances were discriminated between government and non government concepts based on learning on workclass attribute. AOI-HEP mining interest for frequent pattern will be influenced by learning on their chosen attribute. The experiments showed that adult dataset which learn on workclass attribute had AOI-HEP mining interest for frequent pattern and there are four frequent patterns which have strong discrimination rule. Meanwhile, extended experiments upon adult dataset which learn on marital-status attribute showed there is no AOI-HEP mining interest for frequent pattern.

Progress in the complexity of large cities, highly complex systems, and intelligence science, in particular, smart city technology, has shown great ability in helping to reduce traffic congestion in developing cities. All ideas from the development of intelligent transportation to a town that wants to build and want to change into a smart city, especially in the field of ACP (system created, computing developed), based on parallel management and control system (PTMS). PTMS is considered to be enlarged to a new generation of an intelligent transportation system, and its essential component of architecture then make the hardware and software that will support a new architecture in a developing city to a smart city. The case in a lift is a communication system on a car that uses peer to peer networks and smart cards, with a communication system in a vehicle is expected to control congestion in a developing city through an original town with a connected system. This paper proposed four aspects of surveillance such as Traffic surveillance, vehicle Surveillance, passenger surveillance and driver surveillance. The combination of these surveillances will create best applied Intelligent Transportation Systems (ITS) or can be called as Smart Transportation Systems(STS).

No abstract