Advantages And Disadvantages Of Data Mining

Rapid advances in data collection and storage technology have enabled organizations to accumulate vast amount of data. Simple transactions of everyday life such as using a cash card, credit card, a telephone monitoring system or browsing the web lead to automated data storage. In many cases, these large volumes of data can be mined for interesting and relevant information in a wide types of applications. When the quantity of the underlying data is huge, it tends to a number of computational and mining challenges. Currently, a wide class of data-intensive applications, in which data is uses in the form of continuous flow of streams, has been widely recognized. Not only is the volume of the data for these applications unbounded, but the data

Several applications naturally generate data streams: super market, banking sectors, traffic and network monitoring systems, log records or click-streams in web tracking, manufacturing processes, data comes from sensor applications, phone detail records in telecommunications, e-mail messages our internet etc. The basic challenge is that ‘data-intensive’ mining is constrained by bounded availability of resources, time, memory, and data sample size. Data mining has traditionally been performed over static data-items, where data mining algorithms can sustain to read the input data many times. When the source of data items is become continuous data stream, then it become typical that all data can be loaded into the memory and static data mining with a fixed size dataset is no longer technically suitable due to the unique characteristics of streaming data. The following given constraints apply in the Data Stream model: 1. The size of data that has arrived and will approach in the future is extremely large; in fact, the series of arrival is potentially

the problem of mining frequent itemsets in giant databases was first proposed by Agrawal et al. [2] in 1993. Recently, database and data mining communities have cantered on a replacement knowledge model, wherever knowledge arrives within the type of continuous streams. it 's typically spoken data streams or streaming knowledge. Many applications generate large amount of data streams in real time, such as sensor generated data from sensor networks, transaction flows in retail world, A data stream is an ordered series of items that arrives in time. Different from data in traditional static databases and data streams are continuous arrival, unbounded, sometimes escort high speed and have a data distribution that often changes over time. One example application of data stream is to estimate missing data in sensor networks and another one is prediction of data items in super market over continues data. Mining from data stream have following two properties of streams is which is more challenging due to data streams, i.e., the data streams are continuous and unbounded and data in the streams are not necessarily equally distributed; their distributions are usually vary over time period. In recent years, many stream mining algorithms have been proposed, and they can be