CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH

FOR THE COMPUTATION OF THE REGION OCCUPIED BY A

SET OF POINTS

Adriano Moreira and Maribel Yasmina Santos

Department of Information Systems, University of Minho

Campus de Azurém, Guimarães, Portugal

Keywords: Concave hull, convex hull, polygon, contour, k-nearest neighbours.

Abstract: This paper describes an algorithm to compute the envelope of a set of points in a plane, which generates

convex or non-convex hulls that represent the area occupied by the given points. The proposed algorithm is

based on a k-nearest neighbours approach, where the value of k, the only algorithm parameter, is used to

control the “smoothness” of the final solution. The obtained results show that this algorithm is able to deal

with arbitrary sets of points, and that the time to compute the polygons increases approximately linearly

with the number of points.

1 INTRODUCTION

The automatic computation of a polygon that

encompasses a set of points has been a topic of

research for many years. This problem, identified as

the computation of the convex hull of a set of points,

has been addressed by many authors and many

algorithms have been proposed to compute the

convex hull efficiently (Graham, 1972; Jarvis, 1973;

Preparata, 1977; Eddy, 1977). These algorithms

compute the polygon with the minimum area that

includes all the given points (or minimum volume

when the points are in a three-dimensional space). In

this context, given a set of points, there is a single

solution for the convex hull.

For certain applications, however, the convex

hull does not represent well the boundaries of a

given set of points. Figure 1 shows one example. In

this example, where the points could represent trees

in a forest, the region defined by the convex hull

does not represent the region occupied by the trees.

This same problem, or similar problems, has

already been addressed by other authors (e.g.

(Edelsbrunner, 1983; Galton, 2006; Edelsbrunner,

1992a; Edelsbrunner, 1992b; Amenta, 1998)). In

(Edelsbrunner, 1983) the concept of alpha-shapes

was introduced as a solution to this same problem.

The concept of alpha-shape was further developed in

(Edelsbrunner, 1992a; Edelsbrunner, 1992b) and

other solutions, such as crust algorithms (Amenta,

1998), were also proposed. However, most of the

proposed approaches address the reconstruction of

surfaces from sets of points, belonging to that

surface and, therefore, are not optimized for the

referred problem.

Figure 1: The area of the convex hull does not represent

the area occupied by the set of points.

In other words, little work was devoted to the

problem described in this paper, as also recognized

by Galton et al. (Galton, 2006). In their paper,

Galton et al. describe this same problem and present

a few examples of applications that could benefit

from a general solution to compute what they call

the “footprint” of a set of points. They also describe

the existing approaches, including the Swinging

Arm algorithm (SA), and define a criterion with 9

“concerns” to evaluate those solutions. We therefore

refer to this paper for a description of previous work

on this subject.

Moreira A. and Yasmina Santos M. (2007).

CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS.

In Proceedings of the Second International Conference on Computer Graphics Theory and Applications - GM/R, pages 61-68

DOI: 10.5220/0002080800610068

 SciTePress

This paper also addresses this problem, by

proposing a new algorithm for the computation of a

polygon that best describes the region occupied by a

set of points.

The algorithm described in this paper was

developed within the context of the LOCAL project

(LOCAL, 2006) as part of a solution for a broader

problem. The LOCAL project aims to conceive a

framework to support the development of location-

aware applications, and one of its objectives is to

develop a process to automatically create and

classify geographic location-contexts from a

geographic database (Santos, 2006). As part of this

process, we faced the problem of identifying the

“boundaries” of a set of points in the plane, where

the points represent Points Of Interest (POIs).

In order to solve this problem, we developed a

new algorithm to compute a polygon representing

the area occupied by a set of points in the plane.

This new algorithm filled the needs of our research

project and, we believe, can be used in similar

situations where the assignment of a region to a set

of points is required.

This paper is organized as follows: section 2

presents the problem of creation of polygons given a

set of points. Section 3 describes the Concave Hull

algorithm developed for the computation of

polygons with convex and non-convex shapes.

Section 4 introduces the implementation undertaken,

presents some examples of the obtained results, and

discusses performance issues through numerical

evaluation of the implemented algorithm. Section 5

concludes with some remarks and future work.

2 COMPUTING REGIONS’

BOUNDARIES

The problem we faced in the LOCAL project was

how to calculate the boundary of a geographic area

defined by a set of points in the geographic space.

These points represent POIs which are a common

part of geographic databases and navigation systems.

Figure 2 shows an example of an artificial set of

POIs within a given geographic area. In this data set,

one (we, humans) can easily identify 7 different

regions, in addition to a number of “noise” points.

Our goal in the LOCAL project was to

automatically detect these regions, while removing

the noise points, and calculate the polygons that

define the respective boundaries. The final result

should be as shown in Figure 3.

Figure 2: Initial data set.

The approach we adopted to achieve the goal

depicted in Figure 3 was to divide the identification

of the groups of points (identification of the points

belonging to each region and noise removal), from

the calculation of the polygons describing those

regions, as described in (Santos, 2006), and also as

suggested in (Galton, 2006).

Figure 3: The goal.

For the first phase, an implementation of the

Shared Nearest Neighbours (SNN) clustering

algorithm was used (Ertoz, 2003). The SNN is a

density-based clustering algorithm that has as its

major characteristic the fact of being able to detect

clusters of different densities, while being able to

deal with noise and with clusters with non-globular

(and non-convex) shapes. SNN uses an input

parameter, k, which can be used to control the

granularity of the clustering process. The groups of

points depicted in Figure 3 were obtained using

SNN with k=8. The noise points were also discarded

by SNN (slashed points in Figure 3). In this

example, the task of SNN was easy, as the seven

groups of points are clearly separated from the noise

points. However, with real POIs, the regions might

not be so clearly defined and the regions might be of

very “strange” shapes.

The second phase of the process is, for each

group of points found by SNN, to compute the

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

corresponding polygon that defines the boundaries

of the region. In this data set there are two distinct

types of regions: the “circle shaped” regions (A, C

and G), and the other regions with less regular

shapes (B, D, E and F). For the first group, there are

a set of algorithms that could be used to calculate the

convex hull of the points. However, for the other

group of regions, the convex hull approach is not

clearly a good solution, as shown in Figure 1 for the

D region.

In the next section we describe the solution that

was developed to overcome the limitations of the

convex hull approach for this kind of applications.

3 THE CONCAVE HULL

ALGORITHM

The goal of the algorithm described in this section

is, given an arbitrary set of points in a plane, to find

the polygon that best describes the region occupied

by the given points. While there is a single solution

for the convex hull of a set of points, the same is not

true for the “concave hull”. In the statement that

defines our goal (previous paragraph), the

expression “best describes” is ambiguous, as the

“best” might depend on the final application. As an

example, consider the two polygons shown in Figure

4, which describe the region E. Which of the two

polygons, a) or b), “best describes” region E?

Figure 4: Which one is the best? Two polygons for the

same set of points.

Since there are multiple solutions (polygons) for

each set of points, and the “best” solution depends

on the final application, our approach to compute the

polygons should be flexible enough to allow the

choice of one among several possible solutions for

the set of points. The other implication of this

ambiguous definition of “best” is that it turns very

difficult to assess the correctness of any algorithm

used to compute the polygon, and even to compare

different algorithms. For this last purpose, we will

adopt the criteria described in (Galton, 2006) (see

Section 4).

3.1 k-Nearest Neighbours Approach

Our approach to calculate the Concave Hull of a set

of points is inspired in the Jarvis’ March (or “gift

wrapping”) algorithm used in the calculation of the

convex hull (Jarvis, 1973). In this algorithm, the

convex hull is calculated by finding an extreme

point, such as the one with lowest value of Y (in the

yy axis), and then by finding the subsequent points

by “going around” the points – the next point is the

one, among all the remaining points, that is found to

produce the largest right-hand turn.

The approach adopted for the calculation of the

concave hull is similar, except that only the k-nearest

neighbours of the current point (last founded vertex)

are possible candidates to become the next point in

the polygon. Figure 5 illustrates this concept.

The first step of the process is to find the first

vertex of the polygon (point A in Figure 5a) as the

one with the lowest Y value. In the second step, the

k points that are nearest to the current point are

selected as candidates to be the next vertex of the

polygon (points B, C and D in Figure 5a, for k=3). In

this case, point C is selected as the next vertex of the

polygon, since it is the one that leads to the largest

right-hand turn measured from the horizontal line

(xx axis) that includes the first point (point A). Since

C is now a vertex of the polygon (as well as A), it

must be removed from subsequent steps while

searching for the k-nearest neighbours.

In the third step, the k-nearest points of the

current point (point C) are selected as candidates to

be the next point of the polygon (points B, D and E

in Figure 5b). In this case, the point that results in

the largest right-hand turn, corresponding to the

largest angle between the previous line segment and

the candidate line segment, is selected (point E). As

before, point E is now part of the polygon and will

never be considered in the next steps.

The process is repeated until the selected

candidate is the first vertex. For the first vertex

(point A) to be elected as a candidate, it must be

inserted again into the data set after the first four

points of the polygon are computed (before that, if

the first point is selected as the best candidate, a

triangle is computed). By the end of the process, the

polygon is closed with the first and the last point

being the same (point A).

CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION

OCCUPIED BY A SET OF POINTS

a) b)

Figure 5: The k-nearest neighbours approach.

In this example, three candidates were

considered in each step (k=3). If a larger number of

candidates were considered, the computed polygon

would become “smoother”. The number of

neighbours cannot, however, be larger than the

number of remaining points in each step. If, in a

particular step, the number of remaining points

(candidates) is smaller than k, then the algorithm

automatically considers all the remaining points

(without any user intervention).

This approach works for the majority of the

cases. However, there are two special cases that

must be pointed out. One of them is when the

selected candidate results in a polygon edge that

intersects one of the already computed edges. This

case is depicted in Figure 6a. In this example (step

5), the candidate that results into the largest right-

hand turn is point B. However, this candidate leads

to a polygon edge that intersects one of the existing

edges and, therefore, should be discarded. In cases

like this, the next candidate should be considered

(point G in this example). If none of the candidate

points (the k-nearest neighbours) is acceptable, then

a higher number of neighbours must be considered,

by increasing the value of k and starting again.

The other special case may occur when the

spatial density of the initial set of points is not

uniform. Figure 6b illustrates this case with a set of

points where there are clearly two different

“regions”. This case should not be very common if

the initial data set has gone through the clustering

process (e.g using SNN), since, in that case, this data

set would be separated into two different clusters.

Anyway, in an arbitrary data set this special situation

may occur and must be addressed.

Figure 6: Special cases: a) where the new edge intersects

another existing edge of the polygon; b) where the points

are not uniformly distributed in the space.

In this second case, the first point of the polygon

is in the lower-left region (the point with the lowest

Y value) and, therefore, the process starts by looking

for candidates that are near this first point. However,

since the points in the upper-right group are too far

away from the points in the lower-left group, they

are never considered as candidates if the number of

neighbours (value of k) considered in each step of

the process is small. As a consequence, the points in

the upper-right group are left out of the polygon. To

solve this issue, a higher number of neighbours must

be considered. Since the value of k chosen by the

user might be too small, the algorithm must verify,

at the end, that all the points are within the generated

polygon. If not, a higher value of k is automatically

tried by the algorithm using a recursive process that

stops when all the points are within the computed

polygon.

3.2 Concave Hull Algorithm

The steps behind the Concave Hull concept

described in the previous section were used to

develop the algorithm that is shown on the next page

(Algorithm 1).

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

Algorithm 1: The Concave Hull algorithm.

ONCAVEHULL [pointsList, k]

Input. List of points to process (pointsList); number of neighbours (k)

Output

. An ordered list of points representing the computed polygon

1: kk ← Max[k,3] ► make sure k>=3

2: dataset ← CleanList[pointsList] ► remove equal points

3: If Length[dataset] < 3

4: Return[null] ► a minimum of 3 dissimilar points is required

5: If Length[dataset] = 3

6: Return[dataset] ► for a 3 points dataset, the polygon is the dataset itself

7: kk ← Min[kk,Length[dataset]-1] ► make sure that k neighbours can be found

8: firstPoint ← FindMinYPoint[dataset]

9: hull ← {firstPoint} ► initialize the hull with the first point

10: currentPoint ← firstPoint

11: dataset ← RemovePoint[dataset,firstPoint] ► remove the first point

12: previousAngle ← 0

13: step ← 2

14: While ((currentPoint≠firstPoint)or(step=2))and(Length[dataset]>0)

15: If step=5

16: dataset ← AddPoint[dataset,firstPoint] ► add the firstPoint again

17: kNearestPoints ← NearestPoints[dataset,currentPoint,kk] ► find the nearest neighbours

18: cPoints

← SortByAngle[kNearestPoints,currentPoint,prevAngle] ► sort the candidates

(neighbours) in descending order of right-hand turn

19: its ← True

20: i ← 0

21: While (its=True)and(i<Length[cPoints]) ► select the first candidate that does not intersects any

of the polygon edges

22: i++

23: If cPoints

=firstPoint

24: lastPoint ← 1

25: else

26: lastPoint ← 0

27: j ← 2

28: its ← False

29: While (its=False)and(j<Length[hull]-lastPoint)

30: its ← IntersectsQ[{hull

step-1

,cPoints

},{hull

step-1-j

,hull

step-j

}]

31: j++

32: If its=True ► since all candidates intersect at least one edge, try again with a higher number of neighbours

33: Return[ConcaveHull[pointsList,kk+1]]

34: currentPoint ← cPoints

35: hull ← AddPoint[hull,currentPoint] ► a valid candidate was found

36: prevAngle ← Angle[hull

step

,hull

step-1

]

37: dataset ← RemovePoint[dataset,currentPoint]

38: step++

39: allInside ← True

40: i ← Length[dataset]

41: While (allInside=True)and(i>0) ► check if all the given points are inside the computed polygon

42: allInside ← PointInPolygonQ[dataset

,hull]

43: i--

44: If allInside=False

45: Return[ConcaveHull[pointsList,kk+1]] ► since at least one point is out of the computed polygon,

try again with a higher number of neighbours

46: Return[hull] ► a valid hull was found!

CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION

OCCUPIED BY A SET OF POINTS

This algorithm makes use of the following

functions:

CleanList[listOfPoints]: returns the given

listOfPoints with no more than one copy of each point

(removes duplicates).

Length[vector]: returns the number of elements of the

given vector.

FindMinYPoint[listOfPoints]: returns the element

({x,y} pair) of the given listOfPoints with smaller

value of Y.

RemovePoint[vector,e]: returns the given vector

without the given element e.

AddPoint[vector,e]: returns the given vector with

the given element e appended as the last element.

NearestPoints[listOfPoints,point,k]: returns a

vector with the

k elements of listOfPoints that are

closer to the given point. In the current implementation,

this function uses the Euclidean distance to select the

nearest points. However, the distance functions can be

used. This function internally re-computes the value of k

as the minimum value between the given value of k and

the number of points present in the dataset.

SortByAngle[listOfPoints,point,angle]: returns

the given listOfPoints sorted in descending order of

angle (right-hand turn). The first element of the returned

list is the first candidate to be the next point of the

polygon.

IntersectQ[lineSegment1,lineSegment2]: returns

True if the two given lines segments intersect each other,

and False otherwise.

PointInPolygonQ[point,listOfPoints]: returns

True if the given point is inside the polygon defined by

the given listOfPoints, and False otherwise.

4 IMPLEMENTATION AND

RESULTS

The algorithm described in section 3 was

implemented as a Mathematica (Mathematica, 2006)

package, which was used to evaluate the algorithm

and also as a tool to fulfil our project needs. In the

following subsections we present a few examples of

the hulls computed by this algorithm, as well as

some results on its performance. The developed

code (one Mathematica package) is available online

on the web site of the LOCAL project, where the

algorithm can be tried through a web interface.

4.1 Results

The polygons shown in Figure 3 (section 2) and

Figure 4 (section 3) were all computed using the

algorithm described in this paper. In Figure 7 two

other examples are presented.

Figure 7: Two hulls computed by the proposed algorithm.

Figure 7a shows a case where the shape of the

region occupied by the points is very irregular. For

this data set, a value of k=5 was used. The other

case, in Figure 7b, illustrates the result obtained for a

set of points with a large variation in the spatial

density of the points and with two regions. In this

example, the algorithm was started with k=3 but, in

order to include the right-most group of points, the

algorithm automatically increased the value of k up

to 18. Both results were obtained with the lowest

value of k that permits the computation of the

polygon. Using higher values of k would lead to

“smoother” polygons.

The proposed algorithm was already used in a

real application that required the definition of

geographic location contexts that are used to identify

in which particular scenario a mobile user is located.

The definition of the regions was done analysing a

geographic database that integrates a total of 18 914

POIs (Santos, 2006).

4.2 Performance

In order to evaluate the performance of the proposed

algorithm in terms of computational load, the time

used to compute the polygons was measured for

several data sets of different sizes. The used test data

sets were randomly generated within the space of a

circle with unitary radius. For each data set, different

values of k were also used. Each point in the

following graphs was obtained by averaging the

several time values needed to process 20 different

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

data sets. The obtained results are shown in Figure 8

and Figure 9.

10 25 50 100 250 500 1000

number of points

0.1

emit

k=3+

k=10+

k=20+

Figure 8: Time to compute the polygons vs. the number of

points.

3 5 10 20 30

value of k

emit

n=250

n=25

Figure 9: Time to compute the hull vs. the value of k.

In these graphs, the absolute values of the time

used to compute the polygons is of less importance,

since they depend on the used computer. Instead,

these results are intended to assess the trends in the

computing load when some parameters are changed.

Moreover, these results were obtained from a

Mathematica implementation of the algorithm, that

has not been optimised for speed. The results

presented here were obtained by running the

algorithm in an ordinary Pentium 4-M at 2,2 GHz

with 768 Mbytes of RAM.

Figure 8 shows the time (in seconds) used to

compute the hulls for data sets of size 10, 25, 50,

100, 250, 500 and 1000 points. The upper line

represents the time values obtained when the

algorithm was started with k=3. The lower lines

represent the time values when the algorithm was

started with k=10 and k=20, respectively. However,

in the three cases and for some of the data sets, the

algorithm recursively increased the value of k to go

around the special cases described in subsection 3.1.

These results show that the time to compute the

polygons increases approximately linearly with the

number of points (note the log-log scales used in the

graph).

The other result is that the computing time is

smaller for higher values of k. This can be explained

by the fact that, by starting with a higher k (e.g.

k=20), the time to try lower values of k (e.g. 3 to 19)

that might lead to special cases is removed from the

total time. This is better shown in Figure 9, where

the time to compute the hulls is shown as a function

of the initial value of k, for k=3, 5, 10, 20 and 30, for

two different sizes of the data set (25 and 250 points

each). Here it is clear that lower values of k result in

higher computing times. Figure 9 also shows the

standard deviation on the time taken to compute the

different 20 data sets for each value of k. Here, the

general trend is to observe a lower variation for

higher values of k than for lower values.

4.3 More General Assessment

Using the criteria defined in (Galton, 2006), and the

same nomenclature where S denotes the given set of

points and R(S) refers to the proposed region

representing those points, the “concave hull” can be

described as follows:

1. Outliers are not permitted, meaning that all the

points of S are within the computed polygon.

2. There are always points of S on the boundary

of R(S).

3. The computed “concave hull” (polygon) is

topological regular (unless the points are

collinear).

4. The “concave hull” is connected.

5. The “concave hull” is polygonal.

6. The boundary of the “concave hull” is a

Jordan curve (unless the points are collinear).

7. In some cases, such as in region D in Figure 3,

large areas of empty space are excluded from

the “concave hull”, unless a very large value

for k is used. In other cases, such as the one

shown in Figure 7b, the large area of empty

space in the upper-left region of the data set is

maintained within the computed polygon.

8. The generalization of the Concave Hull

algorithm to three dimensions might be

possible, but not easily.

9. The analysis of the computational complexity

of the Concave Hull algorithm is still future

work.

Comparison of the Concave Hull algorithm with

the SA algorithm described in (Galton, 2006)

resulted in the following advantages of the Concave

Hull. First, the use of the Concave Hull does not

require any previous knowledge of the data set in

CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION

OCCUPIED BY A SET OF POINTS

order to choose the value of k. Starting the algorithm

with k=3 always leads to a polygon with the

characteristics described in the above criteria. On the

other hand, if the SA algorithm is started with a too

low value for r, the result may not be a regular

polygon. Therefore, the choice of r for SA requires a

previous knowledge of the data set. This

characteristic of the Concave Hull makes it suitable

to process many data sets representing different

regions, and where the spatial density of points in

each region can be very different. Second, the

Concave Hull algorithm adapts itself to the

variations in the spatial density of the points within

the same data set, as shown in Figure 7b. On the

other hand, it seams that the SA algorithm uses a

constant value of r to select the list of candidates to

become the next vertex of the polygon, therefore not

being able to adapt to variations in the spatial

density of the points.

5 CONCLUSIONS

In this paper we described an algorithm to compute

the “concave hull” of a set of points in the plane.

The algorithm is based in a k-nearest neighbours

approach and is able to deal with arbitrary sets of

points by taking care of a few special cases. The

“smoothness” of the computed hull can also be

controlled by the user through the k parameter.

The presented algorithm has as advantages the

fact that it can deal with non-convex (concave) hulls

as well as convex ones, and the fact that the user can

adapt the polygons to its needs by choosing the k

parameter. The algorithm was implemented as a

Mathematica package, and the obtained results show

that the time to compute the “concave hull”

increases approximately linearly with the number of

points.

Future work on this subject includes the

improvement of the algorithm implementation,

namely through the use of a more efficient function

to calculate the angles depicted in Figure 5, and a

more efficient function to verify if two line segments

intersect each other. The computational complexity

of the proposed algorithm is also a subject for future

analysis.

ACKNOWLEDGEMENTS

This work was developed as part of the LOCAL

project funded by the Fundação para a Ciência e

Tecnologia through grant POSI/CHS/44971/2002,

with support from the POSI program.

REFERENCES

Graham, R.L., 1972, An efficient algorithm for

determining the convex hull of a planar set,

Information Processing Letters 1, 132-133

Jarvis, R.A., 1973, On the identification of the convex hull

of a finite set of points in the plane. Information

Processing Letters 2, 18-21

Preparata, F.P., and Hong, S.J., 1977, Convex hulls of

finite sets of points in two and three dimensions.

Communications of the ACM, 20, 2 (Feb.), 87-93.

Eddy, W.F., 1977, A new convex hull algorithm for planar

sets. ACM Transactions on Mathematical Software, 3,

4 (Dec.), 398-403.

Edelsbrunner, H., Kirkpatrick D.G, and Seidel R., 1983,

On the Shape of a Set of Points in the Plane, IEEE

Transactions on Information Theory, Vol. IT-29, No.

4, July

Galton, A, and Duckham, M., 2006, What is the Region

Occupied by a Set of Points?, Proceedings of the

Fourth International Conference on Geographic

Information Science – GIScience 2006, Munich,

Germany, September 20-23

Edelsbrunner, H., 1992a, Weighted Alpha Shapes,

Technical Report: UIUCDCS-R-92-1760

Edelsbrunner, H., and Mucke, E.P., 1992b, Three-

dimensional Alpha Shapes, In Proceedings of the 1992

Workshop on Volume visualization, p.75-82, Boston,

Massachusets, USA, October 19-20

Amenta, N., Bern, M., Kamvysselis, M., 1998, A New

Voronoi-Based Surface Reconstruction Algorithm,

Proceedings of the 25th annual conference on

Computer graphics and interactive techniques, p.415-

421, July

LOCAL, 2006, http://get.dsi.uminho.pt/local, visited

December 2006.

Santos, M. Y., and Moreira A., 2006, Automatic

Classification of Location Contexts with Decision

Trees, Proceedings of the Conference on Mobile and

Ubiquitous Systems – CSMU 2006, p. 79-88,

Guimarães, Portugal, June 29-30

Ertoz, L., Steinbach, M. and Kumar, V., 2003, Finding

Clusters of Different Sizes, Shapes, and Densities in

Noisy, High Dimensional Data. In Proceedings of the

Second SIAM International Conference on Data

Mining, San Francisco, CA, USA, May

Mathematica, http://www.wolfram.com, visited October

2006.

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications