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5G (fifth generation) is more reliable at a very low cost and provides 10 times more capacity than other generations. Some of the countries namely India, South Korea, San Marino, China have conducted experiments for the implantation of 5G. 5G Wireless Technology used for mobile communication will be expected to be launched in India by 2020. Companies such as Nokia, Ericsson, Intel, AT&T, BT, Qualcomm, Verizon, and Samsung are developing the new wireless mobiles for 5G. High speed, low latency, and high capacity are the main characteristics of 5G for supporting different real-time multimedia. So, there is necessary to develop the 5G enabling technologies. The key enabling technologies used in 5G networks include Device-to-device (D2D) communication, Machine-to-machine (M2M) communication, Millimetre Wave, Quality of Service (QoS), Network Function Virtualization (NFV), Vehicle-to-everything (V2X), Full-Duplex and Green Communication. 5G allows transmitting data at 10-20 Gbps which is 100 times greater than 4G technology which creates new IoT robotic surgery applications. This paper explains about evolution and overview of 5G wireless technology with its features, applications, equipment providers, technological challenges, impact on society, etc. and also describes the architecture of 5G along with its key enabling technologies.

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In the present days, data mining is the advanced research area because it is one of the important step in the knowledge discovery process. This paper presents an experimental study of finding the frequent item sets by classifying the data base transactions into classes by using Decision tree induction based classification and applying Frequent-Pattern (FP) growth on the classes. First, data base transactions are preprocessed by using the pre-processing techniques and those are classified into classes based on information gain. After classifying the transactions into classes, we applied the FP growth algorithm to obtain the frequent or related item sets. This proposed technique is also suitable for heterogeneous data bases. We examined this technique on different types of data bases and by using this technique it have given the accuracy of 96%

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