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Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. [1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data ...
Data Mining Extensions (DMX) is a query language for data mining models supported by Microsoft's SQL Server Analysis Services product. [1] Like SQL, it supports a data definition language (DDL), data manipulation language (DML) and a data query language (DQL), all three with SQL-like syntax. Whereas SQL statements operate on relational tables ...
Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes.
Multiple low data rate signals are multiplexed over a single high-data-rate link, then demultiplexed at the other end. In telecommunications and computer networking, multiplexing (sometimes contracted to muxing) is a method by which multiple analog or digital signals are combined into one signal over a shared medium.
Relational data mining is the data mining technique for relational databases. [1] Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are ...
An alternative to an inverse multiplexer is to use separate links and load sharing of data between them. In the case of IP, network packets could be sent in round-robin mode between each separate link. Advantages of using inverse multiplexing over separate links include: Lower link latency (one single packet can be spread across all links)
This is often a stop-the-world process that traverses a whole table and rewrites it with the last version of each data item. PostgreSQL can use this approach with its VACUUM FREEZE process. Other databases split the storage blocks into two parts: the data part and an undo log. The data part always keeps the last committed version.