ata Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. In many applications, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift.
In telecommunications, asynchronous communication is transmission of data, generally without the use of an external clock signal, where data can be transmitted intermittently rather than in a steady stream
The purpose of channel coding is to maintain the frequency components in the data stream inside the bandwidth determined by the TX loop filter and RX filter.
channel encoder inserts additional information to the transmitted bit stream to facilitate error detection and correction at the receiver. channel decoder is quite opposite to the channel encoder which transmits desired data after the error detection and correction .
data packets is a kind of packets of data. a groop of bits that is together in a groop.
"burble" refers to the gentle sound of water ruining in a stream/brook or when someone gets exited when they are talking and start talking too much and too fast.
Albert Bifet has written: 'Adaptive stream mining' -- subject(s): Data mining
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
mining the data is called data mining. Mining the text is called text mining
No, it is a noun (plural of datum, now almost exclusively used as a singular mass noun).But it is widely used with nouns as an adjunct, e.g. data processing, data stream, data mining.
Some seminar topics related to data mining could include: Introduction to data mining techniques and algorithms Applications of data mining in business intelligence Big data analytics and data mining Ethical considerations in data mining and privacy protection.
Data Mining companies provide such services as mining for data and mining for data two electric bugaloo. They will often offer to resort to underhanded tactics to mine said data.
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
Data warehouse is the database on which we apply data mining.
Simply, Data mining is the process of analyzing data from several sources and converting it into useful data.
One can learn about data mining by visiting the data mining wikipedia page, which has a very comprehensive article about the topic, starting with the etymology and mostly talking about the various uses of data mining.
Data mining
difference between Data Mining and OLAP