Distributed Clustering of Large-Scale Sparse Data
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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, as they struggle to effectively group similar items and users.
Innovation Solution
The use of modified k-means clustering with cosine distance and distributed processing across multiple processors, along with probabilistic data structures like count sketches, to efficiently cluster sparse high-dimensional data and generate groups of similar items and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional clustering methods are used on large-scale data sets, then the implementation is simple, but the processing efficiency is low and scalability is poor
Solution Approach 1:
The patent divides the large-scale data set into multiple partitions that can be processed in parallel across distributed computing nodes. Each partition is independently clustered using modified k-means, and results are aggregated to form the final clustering. This segmentation enables linear scalability with data size while maintaining processing efficiency.
Solution Approach 2:
The patent transforms the clustering problem from a single-dimensional sequential process into a multi-dimensional parallel process by distributing data across multiple computing nodes. It also applies dimensionality reduction techniques to handle high-dimensional sparse vectors efficiently, projecting them into a lower-dimensional space that preserves clustering quality while reducing computational complexity.
2Measurement precision
If conventional clustering methods are used on high-dimensional sparse data, then the algorithm is easy to implement, but the clustering accuracy is poor
Solution Approach 1:
The patent modifies the standard k-means algorithm by changing the distance metric from Euclidean distance to cosine similarity, which is more appropriate for high-dimensional sparse data. It also adjusts the initialization process and convergence criteria to better suit the characteristics of sparse vectors, thereby improving clustering accuracy without requiring complex preprocessing.
Solution Approach 2:
The patent replaces traditional mechanical clustering operations with optimized linear algebra operations that are better suited for sparse matrix computations. It utilizes sparse matrix storage formats and optimized dot product computations to efficiently calculate similarities between high-dimensional vectors, improving both accuracy and performance.
3Speed
If data is processed in a distributed manner across multiple processors, then processing speed improves, but communication overhead increases
Solution Approach 1:
The patent performs preliminary data partitioning and centroid distribution before the main clustering iterations begin. Each computing node receives its data partition and initial centroids in advance, allowing it to work independently during iterations. This preliminary setup minimizes the need for frequent inter-node communication during the computationally intensive clustering process.
Solution Approach 2:
The patent extracts and processes only the necessary information for clustering at each distributed node, rather than exchanging complete data sets. Each node computes local cluster assignments and contributes only essential update information to the global centroid calculations, reducing communication overhead while maintaining processing speed.
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.