The increasing use of regional languages in digital communication has intensified the need for accurate, context-aware spelling detection tools that can handle the language’s rich morphology and complex orthographic patterns. This paper presents a transformer-based framework for Assamese spelling error detection, utilizing three multilingual pre-trained models—IndicBERT v2, XLM-RoBERTa, and mBERT—fine-tuned on a curated dataset of 115,925 word-level samples. Each word is represented as a tuple (word, label, corrected_word), enabling both binary error detection and lexicon-assisted correction. The models were trained using standardised preprocessing, subword tokenisation, and a weighted loss function to address class imbalance. Experimental results demonstrate that XLM-RoBERTa achieves the highest detection performance, with 94.84
High-utility co-location patterns (HUCPs) can effectively reveal valuable knowledge about spatial features and instances hidden in spatial datasets. However, current HUCPs do not consider user reference features. For example, users may be concerned with which facilities within 500 m of a school form a HUCP, but they are not concerned with the utility of the school itself. Therefore, the utility of the school should not be considered when calculating the utility value of the pattern. To address the shortcomings of traditional HUCPs, this work proposes mining high-utility core co-location patterns (HUCCPs), which treat user reference features as core features. Measuring the interestingness of HUCCPs still does not satisfy the downward closure property. If the generating-testing candidate-based methods are used, the candidate search space will be large, resulting in low mining efficiency. This paper adopts a clique-query-based mining method to improve mining efficiency. First, maximal cliques are enumerated from the neighboring instances graph, and then the maximal cliques are combined into a compact hash structure. The keys of this hash serve as the initial candidates. Only those candidates that contain core features are considered. The utility value of a candidate is calculated by querying the hash value, thereby improving the mining efficiency. Extensive experiments were conducted on the real Yelp dataset, and the experimental results demonstrate the effectiveness and efficiency of the proposed method.
Federated data mining has recently been proposed to securely and effectively mine data from multiple sources in the era of big data. In this paper, we address the federated utility mining problem within a horizontal framework comprising multiple clients and a server. We adopt the concept of the two-phase utility mining algorithm and consider the federated mining property to propose a federated two-phase high-utility itemset mining algorithm for discovering globally high-utility itemsets across multiple clients. According to the user-defined utility threshold, each client identifies high transaction-weighted utility itemsets as local candidate itemsets, as the original two-phase utility mining algorithm does, and sends them to the server. The server then aggregates the uploaded local candidate itemsets to determine global candidates, retrieves their actual utility values from clients, and identifies global high-utility itemsets. Thus, each client shares only candidate itemsets and their utility values with the server, rather than the entire database. The transmission can also be encrypted. The proposed approach thus preserves data privacy during the mining process. The results of the numerical experiments demonstrate that the proposed algorithm can remove unnecessary candidates and produce results nearly identical to those of centralized utility mining.
Sequential pattern mining aims to discover frequent ordered patterns in sequence databases, however, treating all items equally ignores their varying importance. Weighted sequential pattern mining (WSPM) addresses this limitation, yet existing algorithms incur high computational costs and severe memory overhead, especially at low support thresholds. We propose WSUI (Weighted Sequential pattern mining Using Index structures), a scalable algorithm that combines compact index structures with tight upper-bound pruning. WSUI introduces frequency-based automatic weighting to eliminate subjective weight assignment, extends the Data-IDList structure with sequence-level metadata for efficient evaluation, and employs a memory-efficient Pseudo-IDList to avoid materialization. Experiments show that WSUI outperforms the state-of-the-art EWSPM in terms of both runtime and memory usage.
Recent network-constrained clustering approaches achieve improved clustering performance over earlier methods such as NS-TBC; however, they still incur considerable computational overhead due to unprioritized expansion and full adjacency-matrix traversal, leading to redundant edge processing and high time complexity. To address these limitations, this paper proposes Fast-NSTBC (Fast Network-Space Topological-Based Clustering), an optimized topology-based clustering algorithm that prioritizes nearest-point expansion and utilizes an adjacency-list representation. These enhancements reduce unnecessary edge expansion and restrict traversal to only adjacent points, rather than all points in the dataset. Experimental results on eight real-world datasets show that Fast-NSTBC significantly outperforms the state-of-the-art NS-TBC, reducing computational cost by 86
