[PDF] Top 20 Parallel Mining of Frequent Itemsets Using FIMN on Neo4j
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Parallel Mining of Frequent Itemsets Using FIMN on Neo4j
... With development of the information technology, the scale of data is increasing quickly. The massive data poses a great challenge for data processing and classification. Random Forest algorithm is a commonly used ... See full document
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Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size
... to mining optimum consistent itemsets quickly in data stream built upon damped ...to frequent itemset mining on data ...stream mining, below massive algorithms projected in that replica ... See full document
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A Survey on Different Techniques for Mining Frequent Itemsets
... Data mining faces a lot of challenges in the big data ...rule mining algorithm is not sufficient to process large data ...memory. Mining the frequent itemset in the dynamic scenarios is a ... See full document
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A Two way Algorithm Method for Mining of Frequent Itemsets Using MapReduce
... MapReduce is a promising scalable programming model for data-intensive applications and scientific analysis. A MapReduce program expresses a large distributed computation as a sequence of parallel operations on ... See full document
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A Review on Various Frequent Itemsets Mining Algorithms
... incorporates frequent items ultrametric tree (FIUT) rather than conventional ...decompose itemsets, the reducers perform combination operations by constructing small ultrametric trees, and the actual ... See full document
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Index Terms Parallel Data Mining, Maximal Frequent Itemsets,
... real frequent itemsets in database with high accuracy in their ...among frequent itemsets discovered in each partition should be high, which implies a high commonality among the maximal ... See full document
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Efficiently Mining Frequent Itemsets using Various Approaches: A Survey
... large itemsets by scanning the database ...locally frequent itemsets, ...of frequent itemsets in ...the itemsets are ...and frequent itemsets generated are not very ... See full document
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A New Dynamic Distributed Algorithm for Frequent Itemsets Mining
... algorithm, parallel FP- growth [13] is a parallelized version of FP-growth [14], and so ...many frequent itemsets mining algorithms, both sequential and distributed, are related to the Apriori ... See full document
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A Survey on Parallel Mining of Frequent Itemsets in MapReduce
... of mining frequent itemsets in a database ...size using a hash ...all itemsets to reduce the time required for scanning ...then using the divide-and-conquer method to extract ... See full document
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FI-DBSCAN: Frequent Itemset Ultrametric Trees with Density Based Spatial Clustering Of Applications with Noise Using Mapreduce in Big Data
... The parallel mining of frequent itemsets (FI) using Frequent Itemset Ultrametric tree (FIUT) with Density Based Spatial Clustering of Applications with Noise (DBSCAN) on ... See full document
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Parallel Binary Approach for Frequent Itemsets Mining
... in parallel where each subtree can be assigned to a ...done using the directive "shared" with OpenMP and we manage the synchronization using a variable lock with type omp_lock_t (see Table ... See full document
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Fast Algorithm for Finding the Value-Added Utility Frequent Itemsets Using Apriori Algorithm
... Utility-Frequent Mining) [7] is based on the fact that utility-frequent itemsets are a special form of frequent ...S. Frequent itemset mining algorithms can be used to ... See full document
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Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm
... set mining is developed in ...of frequent pattern and Apriori ...tree using association rule mining is presented in ...the frequent itemsets without the generation of the ... See full document
6
Comparative Analysis of Frequent Pattern Matching Based On Apriori & Enhanced Algorithms
... for mining association rule, decreases pruning operations of candidate 2-itemsets, thereby saving time and increase ...current mining methods require users to define one or more parameters before ... See full document
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ESW FI: An Improved Analysis of Frequent Itemsets Mining
... classification, frequent pattern-basedclustering [26], and so ...data-stream mining in the first decade of 21 st ...of mining frequent itemsets (FIs) through data streams under the ... See full document
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Analyzing the Usage Pattern of University Website using Apriori Algorithm through Frequent Item set Generation
... clustered using the multi-pass, K-means algorithm based on the linear wiener transformation and finally found it is that K-Apriori algorithm is more efficient compared to Apriori ... See full document
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Intelligence Data Mining Based on Improved Apriori Algorithm
... thresholds, frequent itemsets and non-frequent itemsets are extracted, the number of non-frequent itemsets is reduced, and then confidence, threshold judgment and ... See full document
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HARPP: HARnessing the Power of Power sets for Mining Frequent Itemsets
... 3. Due to their inherent characteristics, both Apriori and FP-Growth along with its successors can only perform efficiently when the dataset resides entirely in main memory [6]. There are no repeated patterns in real ... See full document
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A Theoretical Formulation of Bit Mask Search Mining Technique for mining Frequent Itemsets
... rule mining algorithms such as AprioriTID, Eclat. All the itemsets combinations are generated through candidate key generation or any other permutation combination ...the itemsets are converted into ... See full document
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ABSTRACT: In recent times, customer behaviour models are typically based on data mining of customer data, and
... Data mining provides information about the nature of ...Data mining tells how ―x‖ is related to ―y‖. Data mining find key dimensions in Health care, Customer relationship management and Fraud or ... See full document
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