Welcome to Understanding Link Analysis. The purpose of my site is to discuss the methods behind leveraging visual analytics to discover answers and patterns buried within data sets.

Visual analytics provides a proactive response to threats and risks by holistically examining information. As opposed to traditional data mining, by visualizing information, patterns of activity that run contrary to normal activity surface within very few occurances.

We can dive into thousands of insurance fraud claims to discover clusters of interrelated parties involved in a staged accident ring.

We can examine months of burglary reports to find a pattern leading back to a suspect.

With the new generation of visualization software our team is developing, we can dive into massive data sets and visually find new trends, patterns and threats that would take hours or days using conventional data mining.

The eye processes information much more rapidly when information is presented as images, this has been true since children started learning to read. As our instinct develops over time so does our ability to process complex concepts through visual identification. This is the power of visual analysis that I focus on in my site.

All information and data used in articles on this site is randomly generated with no relation to actual individuals or companies.

The Basis Behind All Relational Analysis

Much like everything we use, the methodology of relational analysis is manual. Some relational analysis, like geospatial analysis started centuries ago when a doctor from a township in England was trying to discover the reason for an outbreak in dysentery.

By using a map of all the fresh water wells in the town and plotting which wells the victims used, this Doctor identified a cluster of related wells all the victims had in common and produced the first discovered geospatial cluster chart.

To understand how analytical software identifies clusters of related entities, and the determines the strength of those relationships, it's important to understand the manual method of association analysis called the association matrix.

Before software, the association matrix was utilized to find commonalities between entities and an event to determine which entities has the strongest association to the event. The method is very easy to use and is still practical today for conducting field analysis by investigators on the fly without any software.

The process starts by drawing a matrix on a piece of graph paper as illustrated below.

Each of the entities involved in the event are listed on the matrix as you review the data or documents. On each occurrence that is discovered a relationship by two or more of the entities, a circle is placed on the grid to establish that relationship. A solid circle indicates a confirmed relationship and a dotted circle indicates an unconfirmed association as illustrated below.

On completion of the association matrix, you can add the total number of explicit and implicit associations at the bottom of the grid to determine centrality or closeness, principals of current social networking analysis.

By translating the association matrix into a visualization, we can see how analytical software through automation, utilizes this theory to show association charts.