Single Nucleotide Polymorphisms (SNPs), a single DNA base varying from one individual to another, are believed to be the most frequent form responsible for genetic differences. Genotype is the conflated information of a pair of haplotypes on homologous chromosomes. Although haplotypes have more information for disease associating than individual SNPs and genotype, it is substantially more difficult to determine haplotypes through experiments. Hence, computational methods which can reduce the cost of determining haplotypes become attractive alternatives. MEC, as a standard model for haplotype reconstruction, is fed by fragments as input to infer the best pair of haplotypes with minimum error to be corrected. It is proved that haplotype reconstruction in MEC model is a NP-Hard problem. Thus, reducing running time and obtaining acceptable result are desired by researchers. Heuristic algorithms and different clustering methods are employed to achieve these goals. In this paper, the idea of combining different methods is presented. A hybrid model, which is employed the efficiency of different serial and parallel models, is suggested. FCA, K-means and neural network are considered as its component. K-means clustering method is used to improve neural network efficiency. Then the results are compared in different datasets.