What are some of the current research trends and open problems in cluster analysis scalability?

One of the current research trends in distributed clustering is the development of asynchronous clustering algorithms that can handle large-scale datasets. Conventional distributed clustering algorithms rely on synchronous communication, which can lead to significant communication overhead and slow down the clustering process. Asynchronous algorithms, on the other hand, allow nodes to operate independently and update their clustering models without waiting for other nodes to finish their computations. Distributed clustering struggles with non-IID data, common in real-world settings. Most algorithms assume identical data distribution, leading to poor results. Researchers aim to develop methods for accurate clustering on diverse data.

A current research trend in subspace clustering is the development of algorithms that can handle high-dimensional data with complex correlations. Standard subspace clustering algorithms are designed to handle data with simple correlations, but real-world data often exhibit complex correlations that cannot be captured by simple models. Researchers are exploring the use of deep learning techniques, such as autoencoders and generative adversarial networks, to learn complex correlations in high-dimensional data. Noisy data hinders subspace clustering accuracy. Researchers are developing methods to identify and remove noise, improving clustering results.

One of the current research trends in stream clustering is the development of algorithms that can handle concept drift, which occurs when the underlying data distribution changes over time. Traditional stream clustering algorithms are designed to handle static data distributions, but real-world data streams often exhibit concept drift, leading to poor clustering performance. Researchers are exploring the use of online learning techniques, such as incremental learning and transfer learning, to adapt to concept drift and provide robust clustering results. High-speed data streams pose a challenge for stream clustering due to their massive volume. Researchers are developing algorithms to provide real-time clustering results for such data.

We use cluster analysis methods to group similar objects, people, etc. Most clustering methods are heuristics based on algorithms with little statistical reasoning behind them (compared to analysis of variance, discriminant analysis, regression, and factor analysis). The clusters are suggested by the data, not defined a priori. This is a blessing and a curse. Without a theoretical framework to guide cluster selection or evaluate them, the most critical part of cluster analysis is selecting the variables on which the clustering is based. Including irrelevant variables may distort clustering solutions. The set of variables selected should reflect the similarity between objects in terms that are relevant to the research problem.

I also propose using Spectral clustering when it becomes to the big data. I have investigated its efficiency as a co-author of this chapter book: Automatic Clustering in Large Sets of Time Series "https://link.springer.com/chapter/10.1007/978-3-319-78325-3_6" It combine spectral analysis of graph Laplacians with simulated annealing to automatically generate optimized clustering.