LAN wire configuration

Cat 5 is required for basic 10/100 functionality, you will want Cat 5e for gigabit (1000BaseT) operation.
The ethernet cable, are 8 color coded wires.
These wires are twisted into 4 pairs of wires, each pair has a common color theme.
One wire in the pair being a solid or primarily solid colored wire and the other being a primarily white wire with a colored stripe (Sometimes ethernet cables won't have any color on the striped wire, the only way to tell which is which is to check which wire it is twisted around).
Examples of the naming schemes used are: Orange (alternatively Orange/White) for the solid colored wire and White/Orange for the striped cable. The twists are extremely important.

RJ45 :-
RJ-45 conductor data cable contains 4 pairs of wires each consists of a solid colored wire and a strip of the same color. There are two wiring standards for RJ-45 wiring: T-568A and T-568B. Although there are 4 pairs of wires, 10BaseT/100BaseT Ethernet uses only 2 pairs: Orange and Green. The other two colors (blue and brown) may be used for a second Ethernet line or for phone connections. The two wiring standards are used to create a cross-over cable (T-568A on one end, and T-568B on the other end), or a straight-through cable (T-568B or T-568A on both ends).
What is RJ stands for?
RJ stands for Registered Jacks. These are used in telephone and data jack wiring registered with FCC. RJ-11 is a 6-position, 4-conductor jack used in telephone wiring, and RJ-45 is a 8-position, 8-conductor jack used in 10BaseT and 100BaseT ethernet wiring.

Data Mining

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence,machine learning, statistics, and database systems.
It is sometimes called Knowledge Discovery in Databases (KDD) .

The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

    (1) Selection
    (2) Pre-processing
    (3) Transformation
    (4) Data Mining
    (5) Interpretation/Evaluation


Data mining involves six common classes of tasks:

1)Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

2)Association rule learning (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

3)Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

4)Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

5)Regression – attempts to find a function which models the data with the least error.
 
6)Summarization – providing a more compact representation of the data set, including visualization and report generation.

In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends.

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

Data mining of government records – particularly records of the justice system (i.e., courts, prisons) – enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.