Sparse Data Clustering via Distributed Segmentation and Sampling
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Solution Overview
Problem
Conventional clustering methods are inefficient when dealing with extremely large data collections, particularly those that are high-dimensional and sparse, such as user interactions with millions of users and hundreds of thousands of items, where most users have not interacted with most content, leading to sparse vectors with many zero values.
Innovation Solution
A modified version of k-means clustering using cosine distance instead of Euclidean distance, combined with distributed processing across multiple processors and the use of compact probabilistic data structures like count sketches to reduce data size and enhance processing efficiency, along with a hybrid approach that includes both top-down and bottom-up analysis methods to identify groups of similar items and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional clustering methods are used on large-scale data, then clustering functionality is provided, but processing efficiency deteriorates due to the sheer volume of data
Solution Approach 1:
The patent segments the large-scale data processing task by distributing clustering operations across multiple computing nodes. Each node processes a partition of the data independently, allowing parallel computation that scales with data volume. This segmentation resolves the efficiency deterioration by converting a single bottlenecked process into multiple concurrent processes.
Solution Approach 2:
The patent introduces sampling as an intermediary step between raw data and full clustering. By first clustering a sampled subset of data to identify candidate clusters, then refining these clusters on the full dataset, the system avoids directly processing all data at full complexity. This intermediary approach maintains accuracy while improving efficiency.
2Measurement precision
If high-dimensional sparse vectors are processed using traditional methods, then complete data analysis is achieved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts and removes zero values from sparse vectors before processing. By representing sparse vectors in a compressed format that stores only non-zero elements and their positions, the system reduces the dimensionality of the data being processed. This extraction eliminates unnecessary computational operations on zero values while preserving all meaningful information for accurate clustering.
Solution Approach 2:
The patent changes the representation parameters of sparse vectors from dense format to compressed sparse row (CSR) format. This parameter change transforms the data structure to optimize for sparse operations, reducing both memory usage and computational complexity while maintaining clustering accuracy on high-dimensional data.
3Speed
If distributed processing is implemented across multiple processors, then processing speed improves, but system complexity increases
Solution Approach 1:
The patent merges the clustering computation with data loading operations in the distributed processing architecture. By combining multiple functions into unified operations and using shared data structures across processing nodes, the system reduces the overall system complexity despite having multiple processors. This merging approach minimizes inter-node communication overhead and simplifies the distributed system architecture.
Data Source
AI summary
Features are provided for the analysis of collections of data and automatic grouping of data having certain similarities. A collection of data regarding user interactions with item-specific content can be analyzed. The analysis can be used to identify groups of items that are of interest to groups of similar users and/or to identify groups of users with demonstrated interests in groups of similar items. Data may be analyzed in a “bottom-up” manner in which correlations within the data are discovered in an iterative manner, or in a “top-down” manner in which desired top-level groups are specified at the beginning of the process. A bottom-up process may also be distributed among multiple devices or processors to more efficiently discover groups when using large collections of data.