RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

An efficient parallel method for mining frequent closed sequential patterns

Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that re-distributes the work, when some processes are out of work to minimize the idle CPU time.Web of Science5174021739

Implementing Sequential Prefixspan Algorithm by Using Static Load Balancing

Repeated series mining is well known and well studied trouble in information mining. The productivity of the formula is used in several alternative regions like chemistry, bioinformatics, and market basket analysis. A completely unique parallel algorithmic rule for mining of frequent sequences supported a static load-balancing is planned. The static load balancing is done by measure the machine time using a probabilistic algorithm. For cheap size of instance, the algorithms deliver the good speedups. The conferred approach is extremely universal: it is often used for static load-balancing of alternative pattern mining algorithms like item set/tree/graph mining algorithms

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

Multi-threaded Implementation of Association Rule Mining with Visualization of the Pattern Tree

Motor Vehicle fatalities per 100,000 population in the United States has been reported to be 10.69% in the year 2012 as per NHTSA (National Highway Traffic Safety Administration). The fatality rate has increased by 0.27% in 2012 compared to the rate in the year 2011. As per the reports, there are many factors involved in increasing the fatality rate drastically such as driving under influence, testing while driving, and various other weather phenomena. Decision makers need to analyze the factors attributing to the increase in an accident rate to take implied measures. Current methods used to perform the data analysis process has to be reformed and optimized to make policies for controlling the high traffic accident rates. This research work is an extension to the data-mining algorithm implementation Most Associated Sequential Pattern (MASP). MASP uses association rule mining approach to mine interesting traffic accident data using a modified version of FP-growth algorithm. Owing to the huge amounts of available traffic accident data, MASP algorithm needs to be further modified to make it more efficient with respect to both space and time. Therefore, we present a parallel implementation to the MASP algorithm. In addition to this, pattern tree and apriori-tid algorithm implementation has been done. The application is designed in C# using .NET Framework and C# Task Parallel Library

Mining of Frequent Item with BSW Chunking

Apriori is an algorithm for finding the frequent patterns in transactional databases is considered as one of the most important data mining problems. Apriori algorithm is a masterpiece algorithm of association rule mining. This algorithm somehow has constraint and thus, giving the opportunity to do this research. Increased availability of the Multicore processors is forcing us to re-design algorithms and applications so as to accomplishment the computational power from multiple cores finding frequent item sets is more expensive in terms of computing resources utilization and CPU power. Thus superiority of parallel apriori algorithms effect on parallelizing the process of frequent item set find. The parallel frequent item sets mining algorithms gives the direction to solve the issue of distributing the candidates among processors. Efficient algorithm to discover frequent patterns is important in data mining research Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. The objective of the Apriori Algorithm is to find associations between different sets of data. It is occasionally referred to as "Market Basket Analysis". Every several set of data has a number of items and is called a transaction. The achievement of Apriori is sets of rules that tell us how often items are contained in sets of data. In order to find more valuable rules, our basic aim is to implement apriori algorithm using multithreading approach which can utilization our system hardware power to improved algorithm is reasonable and effective, can extract more value information