|
43 | 43 |
|
44 | 44 | <body class="auto-style1"> |
45 | 45 |
|
46 | | -<ul class="bg" style="list-style-type: none; margin: 0; font-size: 36pt; padding: 0; overflow: hidden; background-color: #333; position: fixed; top: 0; width: 100%;"> |
| 46 | +<ul class="bg" style="list-style-type: none; margin: 0; font-size: 36pt; padding: 0; overflow: hidden; background-color: silver; position: fixed; top: 0; width: 100%;"> |
47 | 47 | <li style="float: left;"><a href="https://functional-data-clustering.github.io/"><b>Home</b></a></li> |
48 | 48 | <li style="float: left;"><a href="https://functional-data-clustering.github.io/Team.html"><b>Team</b></a></li> |
49 | 49 | <li style="float: left;"><a href="https://github.com/Functional-Data-Clustering" target="_blank"><b>Platform</b></a></li> |
|
70 | 70 | <td>Multivariate</td> |
71 | 71 | <td>Here</td> |
72 | 72 | <td><a href="https://pypi.org/project/CPFcluster/" target="_blank">PyPI</a></td> |
73 | | - <td> Component-wise Peak-Finding (CPF) is an improvement over DCF: (1) |
74 | | - the assignment methodology is improved by applying the density peaks |
75 | | - methodology within level sets of the estimated density; (2) the |
76 | | - algorithm is not affected by spurious maxima of the density and hence is |
77 | | - competent at automatically deciding the correct number of clusters.</td> |
| 73 | + <td> Component-wise Peak-Finding (CPF) is an improvement over DCF: |
| 74 | + (1) the assignment methodology is improved by applying the density peaks methodology within level sets of the estimated density; |
| 75 | + (2) the algorithm is not affected by spurious maxima of the density and hence is competent at automatically deciding the correct number of clusters.</td> |
78 | 76 | </tr> |
79 | 77 | <tr> |
80 | 78 | <td><a href="https://functional-data-clustering.github.io/Tutorials/DCF.html" target="_blank">DCF</a></td> |
|
83 | 81 | <td><a href="https://github.com/tobinjo96/DCFcluster" target="_blank">GitHub</a></td> |
84 | 82 | <td><a href="https://ieeexplore.ieee.org/document/9679016" target="_blank">Density Core Finding</a> (DCF) is able to detect clusters of varying density and irregular shape, and |
85 | 83 | applicable to big data with numerous clusters.<br> |
86 | | - The idea is to detect high-density core regions, each region representing a cluster, and then assign each non-core point to the same cluster as its nearest neighbor |
87 | | - of higher density.</td> |
| 84 | + The idea is to detect high-density core regions, each region representing a cluster, and then assign each non-core point to the same cluster as its nearest neighbor of higher density.</td> |
88 | 85 | </tr> |
89 | 86 | <tr> |
90 | 87 | <td>FAE</td> |
|
98 | 95 | <td>Functional</td> |
99 | 96 | <td>Here</td> |
100 | 97 | <td><a href="https://github.com/mingz628/GPmixture" target="_blank">GitHub</a></td> |
101 | | - <td> GPmixture is for learning mixtures of Gaussian processes. The idea |
102 | | - is to project the functional data into a few orthonormal functions, |
103 | | - perform cluster analysis of the projection coefficients for each |
104 | | - orthonormal fuction, and aggregate individual clusterings into a |
105 | | - concensus clustering.</td> |
| 98 | + <td> GPmixture is for learning mixtures of Gaussian processes. The idea is to project the functional data into a few orthonormal functions, perform cluster analysis of the projection coefficients for each orthonormal fuction, and aggregate individual clusterings into a concensus clustering.</td> |
106 | 99 | </tr> |
107 | 100 | <tr> |
108 | 101 | <td><a href="https://functional-data-clustering.github.io/Tutorials/REM.html" target="_blank">REM</a></td> |
|
0 commit comments