![]() ![]() Finally, we comparing the two filtering techniques and in this two techniques find the result of which one is the best technique. In this paper, we describe a new filtering approach that combines the content-based filter and collaborative filter to capitalize on their respective strengths, and thereby achieves a good performance. Content-based filtering selects information based on semantic content, whereas collaborative filtering combines the opinions of other users to make a prediction for a target user. In this paper, we present the combination of the two filtering techniques including the content-based and collaborative filtering. Content filtering usually works by specifying character strings that, if matched, indicate undesirable content that is to be screened out. Content filtering is used by corporations as part of Internet firewall computers and also by home computer owners, especially by parents to screen the content their children have access to from a computer. On the Internet, content filtering (also known as information filtering) is the use of a program to screen and exclude from access or availability Web pages or e-mail that is deemed objectionable. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. The effectiveness of the proposed Modified Artificial Bee Colony for load balancing dependent and independent tasks (MABC-LBDIID) algorithms in reducing the operational cost of the cloud system is demonstrated by comparing the results with existing HBB-LB. Improve the results of the existing bee colony optimization methods, in this work proposed a novel modified artificial bee colony algorithm which supports for dependent and independent task with modified artificial bee colony algorithm for load balancing tasks. But the major problem of this work well for independent task and it doesn’t work well for dependent task. ![]() Previous work Propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. Load balancing of non preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. ![]() The main objective of load balancing methods is to speed up the execution of applications on resources whose workload varies at run time in unpredictable way. A load balancing algorithm attempts to improve the response time of user’s submitted applications by ensuring maximal utilization of available resources. It is also proposed to conduct comparative analysis of the developed algorithms with the state of art algorithms.Ĭloud computing is an entirely internet-based approach where all the applications and files are hosted on a cloud which consists of thousands of computers interlinked together in a complex manner. It is proposed to conduct experimental analysis on the synthetic and standard data sets. (3) An Apriori-like deterministic pruning approach to progressively prune patterns and cease the search process if necessary. (2) Back-tracking pattern search process to discover approximate occurrences of a pattern under user specified gap constraints. To solve the problem, we propose algorithm with components such as: (1) Data-driven pattern generation approach to avoid generating unnecessary candidates for validation. In this work, we identify a new research for mining the redundant patterns with gap constraints. The most challenging problem is to find repeating patterns with gap constraints. Rapid increase of the sequential data has created the problem of discovering meaningful patterns from sequences. To efficiently discover the redundant pattern, the focus is on developing new algorithms. In this research, a novel method of finding the redundant pattern is proposed. Recent studies in discovering patterns from sequence data have shown the significant impact in many aspects of data mining. ![]()
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