  Genome sequencing projects are rapidly increasing the number of
high-dimensional protein sequence datasets. Clustering a high-dimensional
protein sequence dataset using traditional machine learning approaches poses
many challenges. Many different feature extraction methods exist and are widely
used. However, extracting features from millions of protein sequences becomes
impractical because they are not scalable with current algorithms. Therefore,
there is a need for an efficient feature extraction approach that extracts
significant features. We have proposed two scalable feature extraction
approaches for extracting features from huge protein sequences using Apache
Spark, which are termed 60d-SPF (60-dimensional Scalable Protein Feature) and
6d-SCPSF (6-dimensional Scalable Co-occurrence-based Probability-Specific
Feature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statistical
properties of amino acids to create a fixed-length numeric feature vector that
represents each protein sequence in terms of 60-dimensional and 6-dimensional
features, respectively. The preprocessed huge protein sequences are used as an
input in two clustering algorithms, i.e., Scalable Random Sampling with
Iterative Optimization Fuzzy c-Means (SRSIO-FCM) and Scalable Literal Fuzzy
C-Means (SLFCM) for clustering. We have conducted extensive experiments on
various soybean protein datasets to demonstrate the effectiveness of the
proposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing feature
extraction methods on SRSIO-FCM and SLFCM clustering algorithms. The reported
results in terms of the Silhouette index and the Davies-Bouldin index show that
the proposed 60d-SPF extraction method on SRSIO-FCM and SLFCM clustering
algorithms achieves significantly better results than the proposed 6d-SCPSF and
existing feature extraction approaches.
