Mining Data Stream is Extracts Knowledge from Rapid Data Records

A valuable project is to find the most expensive or most valuable project in the database. By predicting this information, companies can help you understand the sales details of valuable items that can be used to make important decisions such as catalog mapping, cross-marketing, consumer shopping, performance evaluation and so on. Two new algorithms, VIM (price item mining) and TVIM (tree-based value item mining) are proposed in this research. These are used to find valuable items in the data stream.

Web content mining is the mining, extraction, and integration of useful data, information, and knowledge from web page content. Data stream mining is the process of extracting knowledge structures from continuous fast data records. In many applications, mining of data streams can only be read once or a small amount using limited calculation and storage functions. Examples of data streams include computer network traffic, telephone conversations, ATM transactions, web searches, and sensor data.

Data mining is to extract or mining knowledge from a large amount of data. There may be other terms related to data mining, such as knowledge mining, knowledge extraction, data / pattern analysis, data archeology, and data mining. Knowledge discovery as a process includes the following steps. Data cleansing: Eliminate data that is inconsistent with noise. 2. Data integration: It is a place to combine multiple data sources. Data Selection: Search data related to analysis tasks from the database. Data transformation: for example by performing summary or summary operations and converting or merging the data into a form suitable for mining. Data mining: The basic process of applying intelligent methods to extract data patterns. Pattern assessment: Identifies truly interesting patterns representing knowledge based on several interesting measurements.

The term "data mining" is actually misnomer because it aims to extract patterns and knowledge from a large amount of data rather than extracting (excavating) from the data itself. It is also commonly used in all forms of large-scale data processing and information processing (collection, extraction, storage, analysis, and statistics) and all kinds of computer decision making support systems including artificial intelligence (such as machine learning) and business It is a buzzword. smart. Data mining: Practical machine learning tools and techniques of Java (mainly covering machine learning materials) are originally called practical machine learning, and the term data mining was added only for marketing reasons. Normally, more general terms (large scale) data analysis and analysis - or a practical way, artificial intelligence and machine learning - are more appropriate