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Genome sequencing projects are rapidly increasing the number ofhigh-dimensional protein sequence datasets. Clustering a high-dimensionalprotein sequence dataset using traditional machine learning approaches posesmany challenges. Many different feature extraction methods exist and are widelyused. However, extracting features from millions of protein sequences becomesimpractical because they are not scalable with current algorithms. Therefore,there is a need for an efficient feature extraction approach that extractssignificant features. We have proposed two scalable feature extractionapproaches for extracting features from huge protein sequences using ApacheSpark, which are termed 60d-SPF (60-dimensional Scalable Protein Feature) and6d-SCPSF (6-dimensional Scalable Co-occurrence-based Probability-SpecificFeature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statisticalproperties of amino acids to create a fixed-length numeric feature vector thatrepresents each protein sequence in terms of 60-dimensional and 6-dimensionalfeatures, respectively. The preprocessed huge protein sequences are used as aninput in two clustering algorithms, i.e., Scalable Random Sampling withIterative Optimization Fuzzy c-Means (SRSIO-FCM) and Scalable Literal FuzzyC-Means (SLFCM) for clustering. We have conducted extensive experiments onvarious soybean protein datasets to demonstrate the effectiveness of theproposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing featureextraction methods on SRSIO-FCM and SLFCM clustering algorithms. The reportedresults in terms of the Silhouette index and the Davies-Bouldin index show thatthe proposed 60d-SPF extraction method on SRSIO-FCM and SLFCM clusteringalgorithms achieves significantly better results than the proposed 6d-SCPSF andexisting feature extraction approaches.

A Novel Scalable Apache Spark Based Feature Extraction Approaches for Huge Protein Sequence and their Clustering Performance Analysis