نتایج جستجو برای: iterative process mining algorithm
تعداد نتایج: 2040563 فیلتر نتایج به سال:
Supervised clustering is a data mining technique that assigns a set of data to predefined classes by analyzing dataset attributes. It is considered as an important technique for information retrieval, management, and mining in information systems. Since customer satisfaction is the main goal of organizations in modern society, to meet the requirements, 137 call center of Tehran city council is ...
A refined iterative algorithm based on the block arnoldi process for large unsymmetric eigenproblems
This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SP...
We construct one-step iterative process for an α- nonexpansive mapping and a mapping satisfying condition (C) in the framework of a convex metric space. We study △-convergence and strong convergence of the iterative process to the common fixed point of the mappings. Our results are new and are valid in hyperbolic spaces, CAT(0) spaces, Banach spaces and Hilbert spaces, simultaneously.
In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately with a single query all his questions on data. On the contrary, the workflow of the typical user consists in several steps, in which he/she iteratively refines the extracted knowledge by inspecting previous results and posing new queries...
It is commonly accepted that mining frequent subtrees play pivotal roles in areas like Web log analysis, XML document analysis, semi-structured data analysis, as well as biometric information analysis, chemical compound structure analysis, etc. An improved algorithm, i.e. MFPTM algorithm, which based on fusion compression and FP-tree principle, was proposed in this paper to determine a better w...
In this paper, the problem of finding sequential patterns from graph databases is investigated. Two serious issues dealt in this paper are efficiency and effectiveness of mining algorithm. A huge volume of sequential patterns has been generated out of which most of them are uninteresting. The users have to go through a large number of patterns to find interesting results. In order to improve th...
some algorithms for nding common xed point of a family of mappings isconstructed. indeed, let c be a nonempty closed convex subset of a uniformlyconvex banach space x whose norm is gateaux di erentiable and let {tn} bea family of self-mappings on c such that the set of all common fixed pointsof {tn} is nonempty. we construct a sequence {xn} generated by the hybridmethod and also we give the ...
abstract. the main contribution of the current paper is to propose a new effective numerical method for solving the first-order linear matrix differential equations. properties of the legendre basis operational matrix of integration together with a collocation method are applied to reduce the problem to a coupled linear matrix equations. afterwards, an iterative algorithm is examined for solvin...
Frequent Itemset Mining (FIM) is one of the most investigated fields of data mining. The goal of Frequent Itemset Mining (FIM) is to find the most frequently-occurring subsets from the transactions within a database. Many methods have been proposed to solve this problem, and the Apriori algorithm is one of the best known methods for frequent Itemset mining (FIM) in a transactional database. In ...
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