In this era of the digital world, data play a central role and are continuously challenging spectrum efficiency. With the introduction of enriched multimedia user-generated content, the challenges are even more aggravated. In this vein, uplink caching is considered as one of the promising solutions to effectively cater the user’s demands. One of the main challenges for uplink caching is duplication elimination. In this paper, a cache enabled uplink transmission with a duplication elimination scheme is proposed. The proposed scheme matches the mobile’s data to be uploaded with the cached contents both at mobile station (MS) and small base station (SBS). In contrast to existing techniques, the proposed scheme broadcasts the cached contents at an SBS to all the MSs under its footprint. This provides MS an opportunity to exploit the list of cached contents before uploading its data. A MS only uploads its data if it is not already cached at an SBS. This significantly reduces duplication before the real transmission takes place. Furthermore, the proposed technique reduces energy consumption in addition to improving spectral efficiency and network throughput. Besides, a higher caching hit ratio and lower caching miss ratio are also observed as compared to other schemes. The simulation results reveal that the proposed scheme saves 97% energy for SBS, whereas 96–100% energy is saved for MS on average.

Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.

High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state-of-the-art CoHUIM and CoHUI-Miner algorithms in terms of both time and memory consumption.