In this paper, we present Para Miner which is a generic and parallel algorithm for closed pattern mining. Para Miner is built on the principles of pattern enumeration in strongly accessible set systems. Its efficiency is due to a novel dataset reduction technique (that we call EL-reduction), combined with novel technique for performing dataset reduction in a parallel execution on a multi-core architecture. We illustrate Para Miner's genericity by using this algorithm to solve three different pattern mining problems: the frequent itemset mining problem, the mining frequent connected relational graphs problem and the mining gradual itemsets problem. In this paper, we prove the soundness and the completeness of Para Miner. Furthermore, our experiments show that despite being a generic algorithm, Para Miner can compete with specialized state of the art algorithms designed for the pattern mining problems mentioned above. Besides, for the particular problem of gradual itemset mining, Para Miner outperforms the state of the art algorithm by two orders of magnitude.