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In this paper we have presented a new Discrete Particle Swarm Optimization approach to induce rules from the discrete data. The proposed algorithm initializes its population by taking into account the discrete nature of the data. It assigns different fixed probabilities to current, local best and the global best positions. Based on these probabilities, each member of the population updates its position iteratively. The performance of the proposed algorithm is evaluated on five different datasets and compared against 9 different classification techniques. The algorithm produces promising results by creating highly accurate rules for each dataset.