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Functionality Automatic Classification & Taxonomy Generation | Automatic Clustering | Ideas Cloud
Overview
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Automatic Clustering

Organizations can analyze large sets of documents, including unstructured content exchanged in emails, telephone conversations and IMs, and automatically identify inherent themes or information clusters. In essence, automatic clustering creates order out of chaos and delivers instant, high-level visibility into the knowledge base.

IDOL uses the most advanced heuristics, such as quantum clustering, to form these conceptual groupings. Some applications of this feature include: "What's Hot" clusters that automatically detect burning topics in an organization's information assets and "breaking news" clusters that alert users in real-time to new areas of information of individual interest. Automatic clustering optimizes an organization's information flow while eliminating knowledge gaps.

Quantum Clustering

Autonomy technology continues to innovate by applying advanced mathematics to solve technical challenges. Using quantum mathematics to calculate states/concepts within data, conceptual information is more easily and accurately identified. Quantum wave functions are generated around the data, leading to global, stable results without relying on sampling. Quantum clustering also proves to be highly scalable, as the ability to cluster incrementally (instead of recalculating the entire set upon change in data) reduces the amount of information processing required.

Cluster Visualization

Four intuitive Java-based user interfaces for cluster visualization:

Spectrograph Pane

This user interface displays the relationship between clusters in successive periods and sets of data. Clusters are presented as a JSP-based spectrograph: the x-axis represents information over time, enabling users to visualize how clusters develop over a given time period; the y-axis represents the range of concepts defined within the knowledge base. The color and intensity of the lines represent the volume and timeliness of the clusters. Results can be displayed by clicking on the clusters.

Automatic clustering of communications visualized through the Spectrograph Pane
Automatic clustering of communications visualized through the Spectrograph Pane

2D and 3D Cluster Map

The 2D and 3D Cluster Maps are used to identify conceptual similarities and differences between clusters. Based on JSP, the landscapes are generated from the inter-relationships between clusters and the documents contained in those clusters. Navigation features are identical to the spectrograph pane, enabling users to browse clusters with a click of the mouse.

2D Cluster Map
2D Cluster Map
3D Cluster Map
3D Cluster Map

Geo-Cluster Map

IDOL has enhanced its support for geo-efficiency by providing visualization tools to represent document density per location, thus allowing the administrator to quickly assess usage patterns and make informed decisions about load balancing. IDOL's flexible distribution design already allows data to be stored in the most sensible location based on bandwidth, lag time and availability/demand; this added feature aids in achieving maximum performance for a large and globally dispersed number of users and volume of information.

This is a selection of our forthcoming events, please visit our seminars page for more information.

Automatic Hyperlinks provided by IDOL Server 7

This is a small selection of the Autonomy case studies available, please visit our publications site at http://publications.autonomy.com/ for more information.

Automatic Hyperlinks provided by IDOL Server 7

Functionality Automatic Classification & Taxonomy Generation | Automatic Clustering | Ideas Cloud
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