Data mining techniques discovers the novel, valid, frequent pattern from the large data set. The problems of data mining range from association rule mining, classification to feature extraction and others. Now in the era of internet, the data generated can be measured in terabytes or petabytes. This large amount of data contain huge amount of hidden information that can be useful to many businesses. On this account, there is requirement of efficient and cost-effective approaches and techniques of data mining that can handle this large scale data. The cloud computing provide the environments that are suitable for the tasks of large data mining. The cloud data mining has applications in various domains of biology, banking, pharmacy, chemoinformatics, marketing and many more. The cloud computing is the practice that enables access to the shared pool of configurable computing resources which can be dynamically provisioned. It refers to both the applications delivered as service as well as hardware and system software in the data centres that provide those services. The attractive features of cloud computing such as on-demand access, high scalability, reliability, cost savings, low maintenance and energy efficiency bring benefits to both cloud service consumers and providers. 2. RELATED WORK The different cost models for data mining techniques are as following The cost model for distributed data mining in [1] gives the apriori estimates of the response time for the given task considering a specific architectural model. The distributed data mining response time T is given as T = tddm + tki Where tddm is time taken to do mining in distributed environment and tki is time taken to do knowledge integration. The factors that determine tdd... ... middle of paper ... ...that indicates the scale of the current market. The pricing model for frequent users that have long term requirement can be given as PriceSaaSB = PriceSaaS – Rtot *(k1 * time + k2 *no)/Roc Where PriceSaaS is price for short term users, Rtot is total amount of resources, time duration for which user will occupy certain resources, k1 and k2 are time factor and amount factor respectively. The authors introduced the cost model for cloud storaage[] that consider the system’s design access cost, usage cost, variable cost , discount cost and compensation cost. Therefore the total cost of a user in agiven period of time is given as Cij = Cija + Ciju + Cijf - Cijp -Cijb Where Cij is total cost, Cija is access cost, Ciju is usage cost, Cijf is variable cost , Cijp is discount cost and Cijb is compensation cost and i and j are the user level and service model respectively
Data mining is continuingly growing in use throughout many different industries for a variety of business purposes. Some of these industries are financial and banking, healthcare, retail, etc. (Groth, 2000). One of the main uses of data mining is marketing a company’s products and or services to customers (Groth, 2000).
incur unique amounts of cost. Job costing is allocated by the service and allows a detailed price
At this point, is important to note that Big data itself does not represent more large data set of structured and unstructured data; nowadays bigger than ever and in continuous expansion that can be defined as the "problem of big data" (Cox M. & Ellsworth D., 1997). The ability to organize this "problem" given certain parameters and to be able to build a model or representation of a reality taking care of the existing patterns and relationships to find the true value that lies hidden in data is what can be defined as Data mining (DM) (Kadiyala, S. S., & Srivastava, A., 2011).
In the case of making a TCO model, also opportunity costs and present value are taken into account. Taking present value into account means; making a difference between future and past cash outlays. This way the time value of money can be considered when comparing the different alternatives. Opportunity costs finally can be described as:
The new president believes that the key to the new strategy is to be able to understand the true nature (i.e. costs of customers and orders. He feels that if the company is able to tie costs to customers in an accurate manner, it will enable the company to better focus on higher profitability. Major Issues: What is the ' Understand the cost structure of the company. Allocate costs on a per customer and per order basis. Implement a new cost system that will support the new cost allocation methodology.
Data mining is the technique to interpret the data from other perspective and summarize the data so that the data can be useful information. Technically, data mining is a process to identify relations or patterns in the databases to predict the likelihood of future events. According to Eliason et al, there are three systems for healthcare organization to implement the mining data systems. The three systems are the analytics system, the content system and the deployment system. The analytics system is a system that used to collect all data such as patients clinical data, patients financial data, patients satisfactory data and other data. The content system is used to store all medical evidenced data. The deployment system is used to make new organization structure. There are several elements that consist in data mining which are first extract, transform and load transaction data onto the data warehouse system, second, store and manage the data in a multidimensional system, third, provide data access to information technology professionals, forth, analyze the data by application software and lastly, present the data in graph or table format.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
Every interaction your company has with a customer or supplier likely generates a data trail and this data provides a wealth of information for marketers. Extracting that information and getting it into usable shape requires sophisticated data mining tools. One example of this technology is the used by police departments to identify patterns in crime. We will define, explain and discuss main aspects of data mining. Also its benefits and negative issues.
Machine learning techniques represent the main source of data mining algorithms. Most of machine learning methods require data to be resident in memory while executing the analysis algorithm. Due to the huge amounts of the generated streams, it is absolutely a very important concern to deign space efficient techniques that can have only one look or less over the incoming stream.
For example: with the increase of the number of products produced, the cost of operating a machine also increase. Second we have batch level costs which is associated with batches; producing a multiple units of the same product that are processed together is called a batch. The third type is product level costs which arise from any activity in order to support the production of products. The fourth and the last type is facility level costs, this costs cannot be determined with a particular unit, product or batch; this costs are fixed with respect to batches, products and number of units produced. A single measure of volume is used for allocating costs to each service or product in traditional method for example: direct material cost, machine hours, direct labor cost and direct labor hours. A cost driver is an activity that generate costs, it can be generated by two types of costs the first is a particular machine 's running costs where the costs is driven by production volume as machine hours; the second is quality inspection costs where the cost is driven by the number of times the relevant activity occurs as the number of
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge from the data streams. This research work mainly concentrates on how to find the valuable items found in a transactional data of a data stream. In the literature, most of the researchers have discussed about how the frequent items are mined from the data streams. This research work helps to find the valuable items in a transactional data. This is a new research idea in the area of data stream frequent pattern mining. Frequent Item mining is defined as finding the items which are occurring frequently and above the given threshold. Valuable item is nothing but finding the costliest item or most valuable items in a data base. Predicting this information helps businesses to know about the sales details about the valuable items which guide to make important decisions, such as catalogue drawing, cross marketing, consumer shopping and performance scrutiny. In this research work, two new algorithms namely VIM (Valuable Item Mining) and TVIM (Tree based Valuable Item Mining) are proposed for finding the...
The key objective in any data mining activity is to find as many unsuspected relationships between obtained data sets as possible to be able to achieve a better understanding on how the data and its relationships are useful to the data owner. The potential of knowledge discovery using data mining is huge and data mining has been applied in many different knowledge areas such as in large corporations to optimize their marketing strategies or even to smaller scale in medicinal research where data mining is used to find the relationship patient’s data with the corresponding medicinal prescription and symptoms.
- Data mining finds hidden pattern in data sets and association between the patterns. To achieve the objective of data mining association rule mining is one of the important techniques. This paper presents a survey on three different association rule mining algorithms FP Growth, Apriori and Eclat algorithm and their drawbacks which would be helpful to find new solution for the problems found in these algorithms The comparison of algorithms based on the aspects like different support value.
Cloud Computing (CC) is most used terminology in information and communication technology (ICT) in modern years. CC provides revolutionary paradigm of creating new business virtually with accessibility whenever and whatever place. CC utilizes exciting ICT inventions such as virtualized computing, internet and distributed computing, to provide powerfully integrated system. Goggle, Microsoft, IBM and AMAZON are some supplier of (CC) in the ICT business. According to Siclovan (2012), cloud computing is known as an ability to access resources (such as database and application) world widely through network with least time. Infrastructure as a service (IaaS), Platform as a service (PaaS) and Software as a service (SaaS) are the three classification of CC with regard to services. CC is also classified into three parts with regard to users: private CC (enterprise users), public CC (general users) and Hybrid CC (both public and private users). Many products of CC have been used by normal users such as Facebook, Dropbox and SkyDrive. There are some products also created for enterprises such as virtual storage, virtual operating system and SharePoint (email service). However, despite some limitations, cloud computing has great potentials as future framework for enterprise since it offers significant benefits to business owner. Cost saving, availability and flexibility are the main benefits of CC to enterprises but security requires to be guaranteed.