What is FP Growth

In this chapter, data mining approaches are applied on standard IoT dataset to identify relationship among attributes of the dataset. IoT is not an exception; data mining can be used in this domain also. Various rule-based classifiers and unsupervised classifiers are implemented here. Using these approaches relation between various IoT features are determined based on different properties of classification like support, confidence, etc. For classification, a real-time IoT dataset is used, which consists of household figures collected from various sources over a long duration. A brief comparison is also shown for different classification approaches on the IoT dataset. Kappa coefficient is also calculated for these classification techniques to measure the robustness of these approaches. In this chapter, standard and popular power utilization in household dataset is used to show the association between the different intra-data dependency. Classification accuracy of more than 86% is found with the Almanac of Minutely Power Dataset (AMPds) in the present work.