Error Detection and Correction Code Using Density-based Clustering Algorithm

In data area, to achieve good information for decision making, suitable processing of data is needed. Data need to be transferred. They are transferred as a vector which contains features of data. During data transferring, errors may occur. Errors change the features of data vector (instance). In this case, error detection and correction techniques are needed to tackle this issue. If data transferred as groups based on its features, any change in the features of any vector will change the group (cluster) of this vector. So, to cluster an incomprehensible data for operating any method of data mining, an influential technique is needed, and this technique should ensure the correctness of the cluster using error detection and correction codes like Hamming and Golay. This paper presents a technique to detect and correct clustered data after the transfer process to reduce the misclustered instances. The main concept is the reemploying the error-correction Golay code with splitting of the data word and code word to symbols. DENCLUE clustering algorithm is used in the step of clustering as density-based clustering algorithm. Comparison with other related works is performed and the simulation results stated that the proposed technique achieved better performance.