Possibilistic C-Means (PCM) google
PCM partitions an m-dimensional dataset Formula into several clusters to describe an underlying structure within the data. A possibilistic partition is defined as a Formula matrix Formula, where Formula is the membership value of object Formula towards the ith cluster …
The Possibilistic C-Means Algorithm: Insights and Recommendations
A Possibilistic Fuzzy c-Means Clustering Algorithm
PCM and APCM Revisited: An Uncertainty Perspective

FiloDB google
FiloDB is a new open-source distributed, versioned, and columnar analytical database designed for modern streaming workloads.
• Distributed – FiloDB is designed from the beginning to run on best-of-breed distributed, scale-out storage platforms such as Apache Cassandra. Queries run in parallel in Apache Spark for scale-out ad-hoc analysis.
• Columnar – FiloDB brings breakthrough performance levels for analytical queries by using a columnar storage layout with different space-saving techniques like dictionary compression. True columnar querying techniques are on the roadmap. The current performance is comparable to Parquet, and one to two orders of magnitude faster than Spark on Cassandra 2.x for analytical queries. For the POC performance comparison, please see cassandra-gdelt repo.
• Versioned – At the same time, row-level, column-level operations and built in versioning gives FiloDB far more flexibility than can be achieved using file-based technologies like Parquet alone.
• Designed for streaming – Enable easy exactly-once ingestion from Kafka for streaming events, time series, and IoT applications – yet enable extremely fast ad-hoc analysis using the ease of use of SQL. Each row is keyed by a partition and sort key, and writes using the same key are idempotent. FiloDB does the hard work of keeping data stored in an efficient and sorted format.
FiloDB is easy to use! You can use Spark SQL for both ingestion (including from Streaming!) and querying.
Connect Tableau or any other JDBC analysis tool to Spark SQL, and easily ingest data from any source with Spark support(JSON, CSV, traditional database, Kafka, etc.)
FiloDB is a great fit for bulk analytical workloads, or streaming / event data. It is not optimized for heavily transactional, update-oriented workflows.
Introducing FiloDB

Spreading Activation google
Spreading activation is a method for searching associative networks, neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or “activation” and then iteratively propagating or “spreading” that activation out to other nodes linked to the source nodes. Most often these “weights” are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. …