Background & Aims-HCC is the 5 th most prevalent cancer worldwide and the 3 rd most lethal. Dysregulation of alternative splicing underlies a number of human diseases, yet its contribution to liver cancer has not been fully explored. The KLF6 gene is a zinc finger transcription factor that inhibits cellular growth in part by transcriptional activation of p21. KLF6 function is abrogated in human cancers due to increased alternative splicing that yields a dominant negative isoform, 'KLF6 SV1', which antagonizes full-length KLF6 ('KLF6Full')-mediated growth suppression. The molecular basis for stimulation of KLF6 splicing is unknown.

Many techniques have been proposed to implement the Apriori algorithm on MapReduce framework but only a few have focused on performance improvement. FPC (Fixed Passes Combined-counting) and DPC (Dynamic Passes Combined-counting) algorithms combine multiple passes of Apriori in a single MapReduce phase to reduce the execution time. In this paper, we propose improved MapReduce based Apriori algorithms VFPC (Variable Size based Fixed Passes Combined-counting) and ETDPC (Elapsed Time based Dynamic Passes Combined-counting)over FPC and DPC. Further, we optimize the multi-pass phases of these algorithms by skipping pruning step in some passes, and propose Optimized-VFPC and Optimized-ETDPC algorithms. Quantitative analysis reveals that counting cost of additional un-pruned candidates produced due to skipped-pruning is less significant than reduction in computation cost due to the same. Experimental results show that VFPC and ETDPC are more robust and flexible than FPC and DPC whereas their optimized versions are more efficient in terms of execution time.

Mining frequent itemsets from massive datasets is always being a most important problem of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. To enhance the efficiency and scalability of Apriori, a number of algorithms have been proposed addressing the design of efficient data structures, minimizing database scan and parallel and distributed processing. MapReduce is the emerging parallel and distributed technology to process big datasets on Hadoop Cluster. To mine big datasets it is essential to re-design the data mining algorithm on this new paradigm. In this paper, we implement three variations of Apriori algorithm using data structures hash tree, trie and hash table trie i.e. trie with hash technique on MapReduce paradigm. We emphasize and investigate the significance of these three data structures for Apriori algorithm on Hadoop cluster, which has not been given attention yet. Experiments are carried out on both real life and synthetic datasets which shows that hash table trie data structures performs far better than trie and hash tree in terms of execution time. Moreover the performance in case of hash tree becomes worst.

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This article contends that in the booming era of information, analysing users' navigation behaviour is an important task. User identification is considered as one of the important and challenging tasks in the data preprocessing phase of the Web usage mining process. There are three important issues with the reactive strategies of User identification methods that need to be focused: the first is dealing of sharing IP address problem in a proxy server environment, the second is distinguishing users from Web robots, and the third is dealing with huge datasets efficiently. In this article, authors have developed a MapReduce-based User identification algorithm that deals with the above mentioned three issues related to user identification methods. Moreover, the experiment on the real web server log shows the effectiveness and efficiency of the developed algorithm.