This work discusses the problem of generating association rules from a set of transactions in a relational database, taking performance and accuracy of found results as the essential aspects for comparing association mining algorithms. We do a critical analysis of two previously existing methods, Apriori and FP-growth, emphasizing their strengths and weaknesses; and based on this analysis, we p...

In the context of mining frequent itemsets, numerous strategies have been proposed to push several types of constraints within the most well known algorithms. In this paper, we integrate the recently proposed ExAnte data reduction technique within the FP-growth algorithm. Together, they result in a very efficient frequent itemset mining algorithm that effectively exploits monotone constraints.

FP-Growth is a classical data mining algorithm; most of its current implementations are based on programming language's primitive data types for their data structures; this leads to poor readability & reusability of the codes. Weka is an open source platform for data mining, but lacks of the ability in dealing with tree-structured data; JUNG is a network/graph computation framework. Starting fr...

Efficient algorithms to discover frequent patterns are crucial in data mining research. Several effective data structures, such as two-dimensional arrays, graphs, trees, and tries have been proposed to collect candidate and frequent itemsets. It seems as the tree structure is most extractive to storing itemsets. The outstanding tree has been proposed so far is called FP-tree which is a prefix-t...

Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of data, computational and I/O cost. Additionally, the recursive mining process to mine these structures is also too voracious in memory resource...

Association Rule is an important tool for today data mining technique. But this work only concern with positive rule generation till now. This paper gives study for generating negative and positive rule generation as demand of modern data mining techniques requirements. Here also gives detail of “A method for generating all positive and negative Association Rules” (PNAR). PNAR help to generates...

The Byzantine Fault Tolerant (BFT) consensus algorithms have been widely applied in the blockchain systems because of their fault tolerance capability to determine system consistency presence malicious nodes. However, BFT are confronted with low efficiency and scalability problems caused by multiple rounds handshake communication. In this paper, a pipeline-based Fast Pipeline Tolerance algorith...

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored most web search engines. In this paper, we propose an automatic approach for building spatial taxonomy, that models’ the notion adjacency will be used in part geographical query. exploits documents are top retrieved list ...

In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or samplingbased methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-...