The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support and confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. In temporal datasets as the time in which a transaction takes place is important we may find sets of items that are frequent in certain time intervals but not frequent throughout the dataset. These frequent sets may give rise to interesting rules but these can not be discovered if we calculate the supports of the item sets in the usual way. We call here these frequent sets locally frequent. Normally these locally frequent sets are periodic in nature. We propose modification to the Apriori algorithm to compute locally frequent sets and periodic frequent sets and periodic association rules.
In this article, we are going to show how to find out short term forecasts of the total number of COVID-19 cases in India in an easy way. Initially the spread of the disease was observably slow in India. Since the first week of May a highly nonlinear pattern has started to take shape. It can be observed that currently in India the spread pattern is nearly exponential. It can be seen further that the number of cases is still continuing to grow very fast. Therefore, instead of going for rigorous time series analysis, we may opt for looking at the data from a recent date downwards, and short term forecasts based on simple numerical analytical methods can be made accordingly.
There are standard computational and statistical techniques of forecasting the spread pattern of a pandemic. In this article, we are going to show how close the forecasts can be if we use a simple numerical approach that can be worked out using just a scientific calculator. Using a few recent data, short term forecasts can be found very easily. In this numerical technique, we need not make any assumptions, unlike in the cases of using computational and statistical methods. Such numerical forecasts would be nearly perfect unless the pandemic suddenly starts retarding during the period of the forecasts naturally or otherwise.
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