Reducing the Main Memory Consumptions of FPmax* and FPclose
نویسندگان
چکیده
In [4], we gave FPgrowth*, FPmax* and FPclose for mining all, maximal and closed frequent itemsets, respectively. In this short paper, we describe two approaches for improving the main memory consumptions of FPmax* and FPclose. Experimental results show that the two approaches successfully reduce the main memory requirements of the two algorithms, and that in particular one of the approaches does not incur any practically significant extra running time.
منابع مشابه
DCI Closed: A Fast and Memory Efficient Algorithm to Mine Frequent Closed Itemsets
One of the main problems raising up in the frequent closed itemsetsmining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance benefits by any algorithm that adopts...
متن کاملReducing Computational and Memory Cost of Substructuring Technique in Finite Element Models
Substructuring in the finite element method is a technique that reduces computational cost and memory usage for analysis of complex structures. The efficiency of this technique depends on the number of substructures in different problems. Some subdivisions increase computational cost, but require little memory usage and vice versa. In the present study, the cost functions of computations and me...
متن کاملCompareing the effectiveness of pharmacotherapy, transcranial direct current stimulation (TDCS), and combined treatment (TDCS and pharmacotherapy) on reducing major depression symptoms and improvement of working memory in veterans with PTSD
Background and Aim: Depression has a destructive impact on performance of working memory. The purpose of current study was to compare the effectiveness of pharmacotherapy, Transcranial Direct Current Stimulation (TDCS), and combined treatment (pharmacotherapy and TDCS) on reducing major depression symptoms, besides improvement of working memory in veterans with PTSD. Methods: This was a quasi-e...
متن کاملMOHA: A Novel Target Recognition Scheme for WSNs
Macroscopic Object Heuristics Algorithm (MOHA) is a one-shot learning associative memory method for target recognition in wireless sensor networks. This method is able to address pattern displacement and pattern rotation issues. This scheme is also capable of reducing the power and memory consumptions of wireless sensor networks. The experimental results show that the proposed scheme can effect...
متن کاملEthanol impairs memory by reducing the synaptic connection of the hippocampal spatial neurons
Background and Objective: Ethanol has undesirable effects on memory and synaptic communication. However, its impact on the learned spatial memory is unclear. We investigated the damaging effects of ethanol on place neurons of rat’s hippocampal CA1.Materials and Methods: Sixty four male Wistar rats (250 g) were administered high (1-8 g/kg) or low (0.05-0.1 g/kg) doses of ethanol intraperit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004