Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality.

NEWS

Version 3.4.6-5 Changes 2016-12-04

- Expanded the vignette of adaptive histograms to a tutorial.
- Expanded the vignette of optimal univariate k-means clustering to a tutorial.
- Update the time course example in Ckmeans.1d.dp function.
- Fixed a bug in the dynamic programming solution to optimal
*weighted*clustering. This bug affected the first weighted cluster. The code that contains this bug was not used for non-weighted fast optimal clustering. If no weight vector is provided when calling Ckmeans.1d.dp, the clustering result will be the same and remains correct.

2016-10-22

- Added a vignette to visualize examples of adaptive histograms.
- Added a vignette to visualize examples of optimal univariate k-means clustering.

Version 3.4.6-4 Changes 2016-10-21

- Added an equal bin width histogram example to contrast with the adaptive histogram.

2016-10-16

- Moved ahist() function from visualize.R to a new R file ahist.R.

2016-10-15

- Added a breaks argument to ahist() so as use default graphics::hist() but with the capacity to add sticks to the histogram generated.
- Added a skip.empty.bin.color argument to ahist() to gain more control over colors of the histogram bars.

2016-10-12

- Added a data argument to ahist() to provide raw data for visualization.
- Allow x to ahist() to be an object of the class "Ckmeans.1d.dp" to avoid recomputing the clustering if it has already been done. This requires the data for clustering to be provided via the data argument.

2016-10-11

- Added an argument add.sticks=TRUE to ahist() to turn on or off the sticks just above the horizontal axis.
- Added an argument style to ahist() for different styles of adaptive histogram.

Version 3.4.6-3 Changes 2016-10-03

- Updated examples in Ckmeans.1d.dp() manual

Changes 2016-10-01

- Added a new function plot() to visualize the clusters.

2016-09-30

- Updated examples of ahist().
- Added sticks to ahist() to show the original input data.

2016-09-27

- Fixed ahist() when there is only a single bin detected.
- Made ahist() run much faster than the previous version.
- Updated previous examples and added more examples to illustrate the use of ahist() better.

Version 3.4.6-2 Changes 2016-09-25

- Introduced a new function ahist() to generate adaptive histograms corresponding to the optimal univariate k-means clustering.

Version 3.4.6-1 Changes 2016-08-28

- Removed usage of lambda functions in C++ code for compatibility with older versions of C++ compilers.
- Shifted the values of x by median to improve numerical stability.

Version 3.4.6 Changes 2016-05-25

- Implemented an O(kn lg n) algorithm, speeding up the program greatly.

Version 3.4.5 Changes 2016-05-22

- s[j,i] is now computed in constant time based on pre-computed sums of input x and its squares from 0 to i.

Version 3.4.4 Changes 2016-05-22

- Incorporated a numerically stable method for computing sample variance when selecting the number of clusters.
- Improved documentation.
- Removed a typo in describing time complexity.

2016-05-18

- Now Ckmeans.1d.dp() function returns "totss", "tot.withinss", and "betweenss" statistics to summarize the optimal clustering obtained.
- print.Ckmeans.1d.dp() print out the above statistics.

Version 3.4.3 Changes 2016-05-15

- Upgraded to support c++11
- Introduced optimal k-means clustering for weighted data

Version 3.4.2 Changes 2016-05-14

- Implemented backward filling of the dynamic programming matrix to utilize lower bounds for the optimal cluster boundary. This step substantially reduced the runtime by half (two or more times faster).

2016-05-07

- Implemented mathematically proven tighter ranges when searching for cluster boundaries. The runtime of the function is greatly reduced. Most notably, the runtime is roughly constant when number of clusters increases after k=2.
- Integrated all test cases into one single file.

Version 3.4.0 Changes 2016-05-07

- Substantial runtime reduction. Added code to check for an upper bound for the sum of within cluster square distances. This reduced the runtime by half when clustering 100000 points (from standard normal distribution) into 10 clusters.
- Eliminated the unnecessary calculation of (n-1) elements in the dynamic programming matrix that are not needed for the final result. This resulted in enormous reduction in run time when the number of cluster is 2: assigning one million points into two clusters took half a a second on iMac with 2.93 GHz Intel Core i7 processor.
- Included a reference to the first description of the dynamic programming solution by Richard Bellman (1973).

Version 3.3.3 Changes 2016-05-03

- Fixed a bug on cluster assignment when there is only one cluster. This was a bug introduced in version 3.3.2.

Version 3.3.2 Changes 2016-05-03

- Added automatic test cases.
- Removed an incorrect warning message when the number of clusters is equal to the number of unique elements in the input vector.
- Changed from 1-based to 0-based C implementation.
- Optimized the code by reducing overhead. See 22% reduction in runtime to repeatedly cluster seven points into two clusters one million times.

Version 3.3.1 2015-02-10 Changes

- Fixed a problem that prevented Windows compilation (now forced the size_t type to unsigned long in max() function.

Version 3.3.0 2015-02-09 Changes

- Added automated test cases into the package.
- Changed the code to not issue a warning message when the number of clusters is estimated to be 1.
- When lower bound of the number of clusters is greater than the unique number of elements in the input vector, both the min and max numbers of clusters are set to the number of unique number of input values.
- When the upper bound of the number of clusters is greater than the unique number of elements in the input vector, the max number of clusters is set to the number of unique elements in the input vector.
- Use warning() instead of cat() to display warning messages.
- Incorporate changes suggested by a user to speed up the code.
- Revised the examples and documentation to improve usability of the package in general.
- Started the NEWS file.

Version 3.02 2014-03-24 and earlier Changes

- The program now automatically determines the number of clusters from a given range.
- The code is optimized for further speedup.