This paper proposes a K-Main Routes (KMR) approach to summarize spatial network activity. KMR discovers the k shortest paths to cluster activities, generalizing the K-means clustering algorithm for network data. KMR uses network Voronoi diagrams, divide-and-conquer strategies, and pruning to improve performance for applications involving observations along paths in a network, such as locations of pedestrian fatalities. The paper also discusses existing methods like MSGF that can cluster multiple paths, single paths, or no paths, and how KMR aims to improve upon these methods.

1. A K-Main Routes Approach to Spatial Network
Activity Summarization
ABSTRACT:-
Interactions spanning multiple organizations have become an important aspect in
today’s collaboration landscape. Organizations create alliances to fulfill strategic objectives. The
dynamic nature of collaborations increasingly demands for automated techniques and algorithms
to support the creation of such alliances. Our approach bases on the recommendation of potential
alliances by discovery of currently relevant competence sources and the support of semi-automatic
formation. The environment is service-oriented comprising humans and software
services with distinct capabilities. To mediate between previously separated groups and
organizations, we introduce the broker concept that bridges disconnected networks. We present a
dynamic broker discovery approach based on interaction mining techniques and trust metrics.
This paper proposes a K-Main Routes (KMR)
approach that discovers k shortest paths to summarize activities. KMR generalizes K-means for
network space but uses shortest paths instead of ellipses to summarize activities. To improve
performance, KMR uses network Voronoi, divide and conquer, and pruning strategies.
SPATIAL network activity summarization (SNAS) has important applications in domains where
observations occur along linear paths in the network. Depending on the domain, an activity may
be the location of a pedestrian fatality, a carjacking, a train accident, etc. Data summarization is
an important concept in data mining that entails techniques for finding a compact description or
representation of a dataset.

2. SYSTEM ANALYSIS
EXISTING SYSTEM:-
 While existing platforms only support simple interaction models (tasks are assigned to
individuals), social network principles support more advanced techniques such as
formation and adaptive coordination.
 Existing Methods of network-based summarization such as Mean Streets Maximal Sub
graph Finding (MSGF)
 Clumping group activities over multiple paths, a Single path/sub graph or no paths at all.
 Road segments/stretches that pose risks to pedestrians may be conceptualized as linear
concentrations because the generation model of pedestrian fatalities is inherently linear.
PROPOSED SYSTEM:-
 Our approach is based on interaction mining and metrics to discover brokers suitable for
connecting communities in service-oriented collaborations.
 The availability of rich and plentiful data on human interactions in social networks has
closed an important loop, allowing one to model social phenomena
 It use these models in the design of new computing applications such as crowd sourcing
techniques.
 A wide range of computational trust models have been proposed. We focus on social trust
that relies on user interests and collaboration behavior. These networks are established
upon mining and metrics.

3. SYSTEM SPECIFICATIONS
Hardware Requirements:-
 System : pentium iv 2.4 ghz
 Hard Disk : 40 gb
 Ram : 256 MB
Software Requirements:-
 Operating system : Windows 7
 IDE : Microsoft Visual Studio 2008
 Database : Sql server 2005
 Coding Language : C#.NET.