Sorry if this has been answered before but could someone help me with solving the following problem:Each symbol in a dataset has a set of labels. Given a set of labels how can we predict more labels for that set?Or to attempt a more formal wording: let a set of sets $S = \{ s_1, s_2, s_3,  ... \} $ where $ s_i \subseteq L $ and $ L = \{ l_1, l_2, l_3,...,l_m\} $ where $L$ is the finite set of labels. Given a set of labels $Y = \{ y_1 ,..., y_n \} $ where $Y \subseteq X $ what is the probability $ P(r = l_i) \forall l_i \in L$ so that $Y \cup \{r\} \in S $.
Predict set elements based on other elements
machine learning;classification
Maybe you could use some algorithm that solves Market Basket Analysis. The problem is explained here: Market Basket Analysis is a modelling technique based upon the theory  that if you buy a certain group of items, you are more (or less)  likely to buy another group of items. For example, if you are in an  English pub and you buy a pint of beer and don't buy a bar meal, you  are more likely to buy crisps (US. chips) at the same time than  somebody who didn't buy beer.http://albionresearch.com/data_mining/market_basket.phpOne example of such an algorithm is: https://en.wikipedia.org/wiki/Association_rule_learning