Liquid Time Constant Networks (LTCs) and Closed Form Continuous Networks (CFCs) are recent time-continuous RNN models known for superior expressivity and efficiency in time-series prediction and autonomous navigation. This paper provides an accessible overview of these models and investigates their performance on tasks like Atari ’Breakout’ behavior cloning, steering angle prediction, and Global Horizontal Irradiance (GHI) forecasting. We optimize LTC and CFC cells within network structures, comparing them with LSTM. Detailed experiments highlight the impact of various hyperparameters, underscoring the effectiveness of LTCs and CFCs in dynamic prediction tasks.

Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an Anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method’s capability for anytime offline clustering.

Stream Clustering algorithms have gained a lot of importance in the recent past due to rapid rising utilities of IoT systems and applications. Anytime algorithms and frameworks play a key role in handling streams that have data arriving/generating at variable rates. They are capable of handling both slow and fast stream speeds, at the same time generate the result with highest possible accuracy. In this paper, we present AnyStreamKM, which is a framework for anytime k-medoids clustering of data streams. It uses a proposed hierarchical data indexing structure known as AnyKMTree that stores the incoming data from the stream in the form of hierarchy of micro-clusters. AnyKMTree is an adaptation of R-tree with its splitting strategy inspired from the design principles of k-medoids clustering. AnyKMTree not only supports anytime features but is also capable of filtering out noise and outliers. Our experimental analysis establishes that AnyKMTree produces micro-clusters that are more compact and purer than the state-of-the-art methods. Also, when offline k-medoids clustering such as PAM (Partitioning Around Medoids) is applied on the micro-clusters produced by AnyKMTree, the resultant clustering has been found to be of higher quality than the state-of-the-art methods.

Classification of data streams has gained a lot of popularity in recent years owing to its multiple applications. In certain applications like community detection from text feeds, website fingerprinting attack, etc., it is more meaningful to associate class labels with groups of objects rather than the individual objects. This kind of classification problem is known as the set-wise classification problem. The few algorithms available in literature for this problem are budget algorithms, i.e. they are designed to process fixed maximum stream speed, and are not capable of handling variable and high speed streams. We present ANYSC which is the first anytime set-wise classification algorithm for data streams. ANYSC handles variable inter-arrival rate of objects in the stream and performs classification of test entities within any available time allowance, using a proposed data structure referred to as CProf-forest. The experimental results show that ANYSC brings in the features of an anytime algorithm and outperforms the existing approaches.

Mining frequent itemsets from transactional data streams has been vastly studied in literature. The existing algorithms mine frequent itemsets within the stream's constrained environment of limited time and memory. However, none of them are capable of handling varying inter-arrival rates of streams. Moreover, these algorithms are not capable of giving mining results instantaneously, even with compromised accuracy if required, and improve the accuracy with increase in time allowance. These two properties characterize an anytime algorithm. In this paper, we propose AnyFI, which is the first anytime frequent itemset mining algorithm for data streams. We also propose a novel data structure, BFI-forest, which is capable of handling transactions with varying inter-arrival rate. AnyFI maintains itemsets in BFI-forest in such a way that it can give a mining result almost immediately when time allowance to mine is very less and can refine the results for better accuracy with increase in time allowance. Our experimental results show that AnyFI can handle high stream speeds upto 60,000 transactions per second (tps) with recall close to 100%.

Parallelizing data mining algorithms has become a necessity as we try to mine ever increasing volumes of data. Spatial data mining algorithms like Dbscan, Optics, Slink, etc. have been parallelized to exploit a cluster infrastructure. The efficiency achieved by existing algorithms can be attributed to spatial locality preservation using spatial indexing structures like k-d-tree, quad-tree, grid files, etc. for distributing data among cluster nodes. However, these indexing structures are static in nature, i.e., they need to scan the entire dataset to determine the partitioning coordinates. This results in high data distribution cost when the data size is large. In this paper, we propose a dynamic distributed data structure, DD-Rtree, which preserves spatial locality while distributing data across compute nodes in a shared nothing environment. Moreover, DD-Rtree is dynamic, i.e., it can be constructed incrementally making it useful for handling big data. We compare the quality of data distribution achieved by DD-Rtree with one of the recent distributed indexing structure, SD-Rtree. We also compare the efficiency of queries supported by these indexing structures along with the overall efficiency of DBSCAN algorithm. Our experimental results show that DD-Rtree achieves better data distribution and thereby resulting in improved overall efficiency.