In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal Data. The Clustering process is an unsupervised learning method, which classifies longitudinal Data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal Data Clustering, including two general categories of non-parametric methods and model-based methods. PubMed, SCOPUS, ISI, Ovid, and Google Scholar were searched between 2000 and 2021. According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for Clustering longitudinal Data This research, with an overview of the studies done in the field of Clustering, can help researchers as a toolbox to choose various methods of longitudinal Data Clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal Data.

Data mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of Data mining techniques can also result in new threats to privacy and information security. Therefore, a new class of Data mining methods called privacy preserving Data mining (PPDM) has been developed. The aim of researches in this field is to develop techniques those could be applied to Databases without violating the privacy of individuals. In this work we introduce a new approach to preserve sensitive information in Databases with both numerical and categorical attributes using fuzzy logic. We map a Database into a new one that conceals private information while preserving mining benefits. In our proposed method, we use fuzzy membership functions (MFs) such as Gaussian, P-shaped, Sigmoid, S-shaped and Z-shaped for private Data. Then we cluster modified Datasets by Expectation Maximization (EM) algorithm. Our experimental results show that using fuzzy logic for preserving Data privacy guarantees valid Data Clustering results while protecting sensitive information. The accuracy of the Clustering algorithm using fuzzy Data is approximately equivalent to original Data and is better than the state of the art methods in this field.

Over recent decades, there has been a growing interest in semi-supervised Clustering. Compared to the supervised or unsupervised Clustering methods for solving different real-life problems, reviewed articles show that semi-supervised Clustering methods are more powerful, and even a small amount of supervised information can significantly improve the results of unsupervised methods. One popular method of incorporating partial supervised information is through labeled Data. In this study, we propose a semi-supervised Clustering algorithm called ConvexClust. The proposed method improves Data Clustering using a geometric view borrowed from the Lune concept in the connectivity index and 10% of labeled Data. Clustering starts with the use of labeled Data and the formation of a convex hull. It continues over the labeling of non-labeled Data and the updating of the convex hull in an iterative process. Evaluations of three UCI Datasets and sixteen artificial Datasets show that the proposed method outperforms the other semi-supervised and traditional Clustering techniques.