P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Abstract

Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable.

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

“…The detailed analysis and classification are sequentially presented in Section 3.2. Specifically, there are five studies related to hierarchical clustering, with four of them based on rough-set theory (MTMDP [38], MGR [41], MNIG [47], and HPCCD [96]), except the P-ROCK [106]. Two studies focus on agglomerative hierarchical clustering, while the remaining three focus on divisive hierarchical clustering.…”

“…Many algorithms performed scalability testing, such as those mentioned in references [40,41,56,63,64,67,68,70,96,98,106], aiming to improve clustering methods for highdimensional data. Notably, algorithms like SCC [61] and SKSCC [76] utilize probabilistic distance functions based on kernel density estimation to increase clustering performance.…”

“…In contrast to the aforementioned variations of hierarchical-based clustering, the algorithm proposed by Altameem et al [106] stands out. Their approach aims to modify the ROCK algorithm [107] by allowing user-defined parameters as input, thus enhancing the flexibility of the algorithm.…”

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

“…ROCK is a hierarchical clustering algorithm that constructs a hierarchy of clusters in the dataset through a bottom-up approach [51,[85][86][87]. Initially, it treats every object as a solo cluster and then merges the more similar clusters to form a new cluster.…”