Selecting balanced clusters of descriptive vectors

1. A method comprising:

accessing, by one or more processors, descriptive vectors that describe items, each descriptive vector having multiple dimensions whose values indicate extents to which multiple characteristics are present in a different item among the items;

calculating, by the one or more processors, vector distances between pairs of the descriptive vectors;

generating, by the one or more processors, a hierarchy of vector clusters by clustering the descriptive vectors into the vector clusters based on the calculated vector distances;

determining, by the one or more processors, centroid vectors of the vector clusters in the hierarchy by calculating mean vectors of the vector clusters, each mean vector and each centroid vector representing a different vector cluster in the hierarchy;

summing, by the one or more processors, inter-cluster vector distances between pairs of the centroid vectors;

summing, by the one or more processors, intra-cluster vector distances between pairs of descriptive vectors in each of the vector clusters;

calculating, by the one or more processors, scores of the hierarchy based on the summed inter-cluster vector distances and on the summed intra-cluster vector distances, each of the scores being calculated based on a different scalar among a plurality of scalars among which a scalar corresponds to an extreme score among the calculated scores; and

selecting, by the one or more processors, a subset of the vector clusters in the hierarchy based on the scalar that corresponds to the extreme score.

2. The method of claim 1 , further comprising:

accessing the items prior to the accessing of the descriptive vectors, each of the items including different media content; and

determining the descriptive vectors by generating a different descriptive vector for each of the items, the generating of each different descriptive vector including analyzing the media content in the corresponding item to be described.

3. The method of claim 2 , wherein:

the accessed items are media items;

the method further comprises normalizing the media items by at least one of:

omitting duplicate media items, omitting non-original media items, omitting media items released on compilation albums, omitting media items recorded at live performances, or retaining media items recorded in studios; and

the determining of the descriptive vectors is performed by generating a different descriptive vector for each of the normalized media items.

4. The method of claim 1 , wherein:

the calculating of the vector distances between the pairs of the descriptive vectors is based on correlations among the descriptive vectors.

5. The method of claim 1 , wherein:

the calculating of the vector distances between the pairs of the descriptive vectors includes calculating quadratic-chi histogram distances between the pairs of the descriptive vectors.

6. The method of claim 1 , wherein:

the clustering of the descriptive vectors is performed according to an agglomerative hierarchical clustering algorithm.

7. The method of claim 6 , wherein:

the agglomerative hierarchical clustering algorithm includes a complete-linkage clustering algorithm.

8. The method of claim 1 , wherein:

the calculating of each score of the hierarchy includes:

selecting a scalar between zero and unity;

multiplying the scalar by the summed intra-cluster vector distances to obtain a first multiplicative product;

multiplying the summed inter-cluster vector distances by the scalar subtracted from unity to obtain a second multiplicative product; and

adding the first multiplicative product to the second multiplicative product to obtain the score of the hierarchy.

9. The method of claim 8 , wherein:

the items are media items released in a set of albums by a same artist; and

the selecting of the scalar is based on a count of albums in the set of albums by the same artist.

10. The method of claim 1 , further comprising:

modifying the selected subset of the vector clusters in the hierarchy, the modifying of the selected subset including:

calculating weights of vector clusters in the selected subset, a first calculated weight corresponding to a first vector cluster in the selected subset; and

removing a first vector cluster from the selected subset based on the first calculated weight failing to transgress a threshold percentile of the calculated weights of the vector clusters in the selected subset.

11. The method of claim 10 , wherein:

the calculating of the weights of vector clusters in the selected subset is based on sizes of vector clusters in the selected subset, the first calculated weight being calculated based on a count of descriptive vectors in the first vector cluster within the selected subset.

12. The method of claim 10 , wherein:

the calculating of the weights of vector clusters in the selected subset is based on average popularity scores of vector clusters in the selected subset, the first calculated weight being calculated based on an average of a group of popularity scores that correspond to a group of items described by at least some descriptive vectors in the first vector cluster within the selected subset.

13. The method of claim 10 , wherein:

the calculating of the weights of vector clusters in the selected subset is based on values of most dominant dimensions of descriptive vectors in vector clusters in the selected subset,

the first vector cluster having a first centroid vector among the centroid vectors,

the first calculated weight being calculated based on a ratio of a most dominant value of a most dominant dimension in the first centroid vector of the first vector cluster to a sum of less dominant values of less dominant dimensions in the first centroid vector of the first vector cluster.

14. The method of claim 1 , further comprising:

generating labels that identify vector clusters in the selected subset of the hierarchy,

a first label identifying a first vector cluster in the selected subset,

the first vector cluster having a first centroid vector among the centroid vectors,

the first label being generated by:

determining a set of most dominant dimensions in the first centroid vector of the first vector cluster, the set of most dominant dimensions having most dominant values in the first centroid vector;

accessing a database that maps the set of most dominant dimensions to corresponding textual descriptors; and

incorporating the textual descriptors into the first label.

15. The method of claim 1 , wherein:

the descriptive vectors that describe the items are mood vectors that describe media items all recorded by a same artist, each mood vector indicating extents to which multiple emotions are perceivable in a different media item among the media items;

the hierarchy of vector clusters is a nested hierarchy of mood clusters that group the mood vectors; and

the selected subset of the mood clusters represents a tier among multiple tiers of the nested hierarchy, the centroid vectors of the selected mood clusters describing and representing the same artist.

16. The method of claim 1 , wherein:

the items described by the descriptive vectors have a common source;

the selected subset of the vector clusters is representative of the common source of the items; and the method further comprises:

storing identifiers of centroid vectors of vector clusters in the selected subset, the identifiers being stored with a contemporary timestamp in an evolutionary history of items attributed to the common source.

17. The method of claim 1 , wherein:

the items described by the descriptive vectors are sourced from multiple sources that include a first source and a second source;

the selected subset of the vector clusters has a first portion that is representative of the first source of the items and has a second portion that is representative of the second source of the items; and the method further comprises:

determining that the first source represented by the first portion of the selected subset is distinct from the second source; and

causing presentation of a notification that the first and second sources are different.

18. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

accessing descriptive vectors that describe items, each descriptive vector having multiple dimensions whose values indicate extents to which multiple characteristics are present in a different item among the items;

calculating vector distances between pairs of the descriptive vectors;

generating a hierarchy of vector clusters by clustering the descriptive vectors into the vector clusters based on the calculated vector distances;

determining centroid vectors of the vector clusters in the hierarchy by calculating mean vectors of the vector clusters, each mean vector and each centroid vector representing a different vector cluster in the hierarchy;

summing inter-cluster vector distances between pairs of the centroid vectors;

summing intra-cluster vector distances between pairs of descriptive vectors in each of the vector clusters;

calculating scores of the hierarchy based on the summed inter-cluster vector distances and on the summed intra-cluster vector distances, each of the scores being calculated based on a different scalar among a plurality of scalars among which a scalar corresponds to an extreme score among the calculated scores; and

selecting a subset of the vector clusters in the hierarchy based on the scalar that corresponds to the extreme score.

19. The non-transitory machine-readable storage medium of claim 18 , wherein:

the selecting of the subset of the vector clusters in the hierarchy includes determining that the scalar that corresponds to the extreme score corresponds to a minimum score among the calculated scores; and

the selected subset of the vector clusters represents a tier among multiple tiers of the hierarchy.

20. A system comprising:

one or more processors; and

a memory storing instructions that, when executed by at least one processor among the one or more processors, cause the system to perform operations comprising:

accessing descriptive vectors that describe items, each descriptive vector having multiple dimensions whose values indicate extents to which multiple characteristics are present in a different item among the items;

calculating vector distances between pairs of the descriptive vectors;

generating a hierarchy of vector clusters by clustering the descriptive vectors into the vector clusters based on the calculated vector distances;

determining centroid vectors of the vector clusters in the hierarchy by calculating mean vectors of the vector clusters, each mean vector and each centroid vector representing a different vector cluster in the hierarchy;

summing inter-cluster vector distances between pairs of the centroid vectors;

summing intra-cluster vector distances between pairs of descriptive vectors in each of the vector clusters;

calculating scores of the hierarchy based on the summed inter-cluster vector distances and on the summed intra-cluster vector distances, each of the scores being calculated based on a different scalar among a plurality of scalars among which a scalar corresponds to an extreme score among the calculated scores; and

selecting a subset of the vector clusters in the hierarchy based on the scalar that corresponds to the extreme score.