DATA MINING:

-
  • Data mining (sometimes called data or knowledge discovery) is the computational  process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both.
  • The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
  • Data mining always depends on effective data collection and warehousing as computer processing.
  • Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified, based upon the end user queries Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
  • Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought.
    • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
    • Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
    • Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
    • Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
  • The Different levels of analysis are used by data mining softwares include the following:
    • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
    • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
    • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID).
    • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
    • Rule induction: The extraction of useful if-then rules from data based on statistical significance.
    • Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.
  • Data mining process consists of five major elements:
    • Extract, transform, and load transaction data onto the data warehouse system.
    • Store and manage the data in a multidimensional database system.
    • Provide data access to business analysts and information technology professionals.
    • Analyze the data by application software.
    • Present the data in a useful format, such as a graph or table.

Comments

Post a Comment

Popular posts from this blog

Pentium microprocessors

-
Image
A 32-bit microprocessor introduced by Intel in 1993. It contains 3.3 million transistors. Since 1993, Intel has developed the Pentium III and more recently the Pentium 4 microprocessors.   Pentium III Pentium III Mainboard Layout Intel builds on the technology it developed with the Pentium II microprocessors.  The Pentium III processor comes with a Synchronized Dynamic Random Access Memory (SDRAM), allowing for an extremely fast transfer of data between the microprocessor and the memory. 70 new instructions, called Streaming SIMD Extensions, enhance multimedia and 3D performance.  Launched February 1999 - Available in speed levels of 450, 500, 550, and 600MHz. 32KB of Level 1 Cache (operating at CPU's full core speed). 512KB of Level 2 Cache (operating at ½ of CPU's core speed). 100 MHz bus speed. The branch prediction/recovery pipeline was doubled to include 10-stages from the P-II.   Pentium IV The next generation of microprocessors from Intel. Pe
Read more

Multilevel Organization of Cache Memory

-
Image
A multilevel cache hierarchy consists of n levels of caches. C1, C2, .......,Ci,....... Cn. A processor reference is serviced by the cache closest to the processor that contains the data.  At the same time that cache provides information to the caches on the path between itself and the processor. Multilevel cache hierarchy for multi- processors neither a local LRU nor a global LRU. Where all references to a Ci cache are percolated to its parent for rear-ranging the LRU stack at the Ci+i level. Multilevel caches Another issue is the fundamental tradeoff between cache latency and hit rate.  Larger caches have better hit rates but longer latency. To address this tradeoff, many computers use multiple levels of cache, with small fast caches backed up by larger, slower caches. Multi-level caches generally operate by checking the smallest level 1 (L1) cache first. If it hits, the processor proceeds at high speed.  If the smaller cache misses, the next larger cache (L2) is c
Read more